From b17e1b85599d09d90d2411a8d94681684014c486 Mon Sep 17 00:00:00 2001 From: tobirohrer Date: Fri, 6 Oct 2023 22:47:35 +0200 Subject: [PATCH 1/4] added time varying price profile to building simulation --- README.md | 61 +- .../building_simulation.py | 52 +- .../data/data_preprocessing.ipynb | 59 +- .../electricity_price_profile.csv | 8761 ++++++++++++++++ ...ion_and_spot_prices_in_Germany_in_2022.csv | 8762 +++++++++++++++++ .../environment.py | 32 +- building_energy_storage_simulation/utils.py | 4 +- docs/source/guide/installation.rst | 41 - docs/source/guide/mdp_definition.rst | 55 - docs/source/index.rst | 10 +- ...ement_learning_sample_implementation.ipynb | 659 +- setup.py | 2 +- tests/test_building_simulation.py | 19 + tests/test_environment.py | 2 +- 14 files changed, 17711 insertions(+), 808 deletions(-) create mode 100644 building_energy_storage_simulation/data/preprocessed/electricity_price_profile.csv create mode 100644 building_energy_storage_simulation/data/raw/energy-charts_Electricity_production_and_spot_prices_in_Germany_in_2022.csv delete mode 100644 docs/source/guide/installation.rst delete mode 100644 docs/source/guide/mdp_definition.rst diff --git a/README.md b/README.md index 02180bf..9d3c25d 100644 --- a/README.md +++ b/README.md @@ -4,14 +4,13 @@ isolated -The Building Energy Storage Simulation serves as open source OpenAI gym (now [gymnasium](https://github.com/Farama-Foundation/Gymnasium)) environment for Reinforcement Learning. The environment represents a building with an energy storage (in form of a battery) and a solar energy system. The aim is to control the energy storage so that the self consumption of the energy generated by the solar energy system is optimized. +The Building Energy Storage Simulation serves as OpenAI gym (now [gymnasium](https://github.com/Farama-Foundation/Gymnasium)) environment +for Reinforcement Learning. The environment represents a building with an energy storage (in form of a battery) and a +solar energy system. The building is connected to a power grid with time varying electricity prices. The task is to +control the energy storage so that the total cost of electricity are minimized. The inspiration of this project and the data profiles come from the [CityLearn](https://github.com/intelligent-environments-lab/CityLearn) environment. Anyhow, this project focuses on the ease of usage and the simplicity of its implementation. Therefore, this project serves as playground for those who want to get started with reinforcement learning for energy management system control. -## Documentation - -The documentation is available at [https://building-energy-storage-simulation.readthedocs.io/](https://building-energy-storage-simulation.readthedocs.io/) - ## Installation By using pip just: @@ -56,19 +55,26 @@ The simulation contains a building with an energy load profile attached to it. T - primarily using electricity generated by the solar energy system, - and secondary by using the remaining required electricity "from the grid" -The simulated building contains a battery which can be used to store energy. By **charging** energy, you can (temporarily) -increase the energy demand of the building, and by **discharging** you can (temporarily) decrease the energy demand of -the building. **The task is to find strategies of when to charge and when to discharge the battery.** Hereby, -the goal is to utilize the battery so energy usage from the solar energy system is maximized and usage of the energy -grid is minimized. Note, that excess energy from the solar energy system which is not used by the electricity load or -used to charge the battery is considered lost. So better use the solar energy to charge the battery in this case ;-) +When energy is taken from the grid, costs are incurred that can vary depending on the time (if a price profile is passed +as `electricity_price` to `BuildingSimulation`). The simulated building contains a battery which be controlled by +**charging** and **discharging** energy. The goal is to find control strategies optimize the use of the energy storage +by e.g. charging whenever electricity prices are high or whenever there is a surplus of solar generation. It is important +to note that no energy can be fed into the grid. This means any surplus of solar energy which is not used to charge the +battery is considered lost. + +### Reward +$$ r_t = -1 * electricity\_consumed_t * electricity\_price_t$$ + +Note, that the term `electricity_consumed` cannot be negative. This means, excess energy from the solar +energy system which is not consumed by the electricity load or by charging the battery is considered lost +(`electricity_consumed` is 0 in this case). ### Action Space -| Action | Min | Max | -| ----------- | ----------- | ----------- | -| Charge | -1 | 1 | +| Action | Min | Max | +|----------|----------|--------| +| Charge | -1 | 1 | The actions lie in the interval of [-1;1]. The action represents a fraction of the maximum energy which can be retrieved from the battery (or used to charge the battery) per time step. @@ -78,30 +84,27 @@ The actions lie in the interval of [-1;1]. The action represents a fraction of t ### Observation Space -| Index | Observation | Min | Max | -| ----------- | ----------- | ----------- | ----------- | -| 0 | State of Charge (in kWh)| 0| Max Battery Capacity | -| [1; n]| Forecast Electric Load (in kWh) | 0 | Max Load in Profile | -| [n+1; 2*n]| Forecast Solar Generation (in kWh) |0| Max Solar Generation in Profile | +| Index | Observation | Min | Max | +|-------------|---------------------------------------|---------------------------|------------------------------| +| 0 | State of Charge (in kWh) | 0 | `battery_capacity` | +| [1; n] | Forecast Electric Load (in kWh) | Min of Load Profile | Max of Load Profile | +| [n+1; 2*n] | Forecast Solar Generation (in kWh) | Min of Generation Profile | Max of Generation Profile | +| [2n+1; 3*n] | Electricity Price (in € cent per kWh) | Min of Price Profile | Max of Price Profile | The length of the observation depends on the length of the forecast used. By default, the simulation uses a forecast length of 4. -This means 4 time steps of an electric load forecast and 4 time steps of a solar generation forecast are included in the observation. +This means 4 time steps of an electric load forecast, 4 time steps of a solar generation forecast and 4 time steps of the +electric price profile are included in the observation. In addition to that, the information about the current state of charge of the battery is contained in the observation. The length of the forecast can be defined by setting the parameter `num_forecasting_steps` of the `Environment()`. -### Reward - -As our goal is to use as less energy as possible, the reward is defined by the energy consumed at every time step (times -1): +### Episode Ends -$$ r_t = -1 * electricity\_consumed_t $$ +The episode ends if the `max_timesteps` of the `Environment()` are reached. -It is important to note, that the term `electricity_consumed` cannot be negative. This means, excess energy from the solar -energy system which is not consumed by the electricity load or by charging the battery is considered lost -(`electricity_consumed` is 0 in this case). +## Code Documentation -### Episode Ends +The documentation is available at [https://building-energy-storage-simulation.readthedocs.io/](https://building-energy-storage-simulation.readthedocs.io/) -The episode ends if the `max_timesteps` of the `Environment()` are reached. diff --git a/building_energy_storage_simulation/building_simulation.py b/building_energy_storage_simulation/building_simulation.py index b3ac910..ec89108 100644 --- a/building_energy_storage_simulation/building_simulation.py +++ b/building_energy_storage_simulation/building_simulation.py @@ -1,4 +1,4 @@ -from typing import Tuple, Iterable +from typing import Tuple, Iterable, Union import numpy as np @@ -14,6 +14,8 @@ class BuildingSimulation: :type electricity_load_profile: Iterable :param solar_generation_profile: Generation profile in kWh per time step. :type solar_generation_profile: Iterable + :param electricity_price: Electricity price in € cent per kWh. + :type electricity_price: Iterable or float :param battery_capacity: The capacity of the battery in kWh. :type battery_capacity: float :param max_battery_charge_per_timestep: Maximum amount of energy (kWh) which can be obtained from the battery or @@ -24,17 +26,24 @@ class BuildingSimulation: def __init__(self, electricity_load_profile: Iterable = load_profile('electricity_load_profile.csv', 'Load [kWh]'), solar_generation_profile: Iterable = load_profile('solar_generation_profile.csv', 'Generation [kWh]'), + electricity_price: Union[Iterable, float] = load_profile('electricity_price_profile.csv', 'Day Ahead Auction'), battery_capacity: float = 100, max_battery_charge_per_timestep: float = 20 ): + self.electricity_load_profile = np.array(electricity_load_profile) self.solar_generation_profile = np.array(solar_generation_profile) + + if isinstance(electricity_price, (float, int)): + self.electricity_price = np.full(len(self.solar_generation_profile), electricity_price) + else: + self.electricity_price = np.array(electricity_price) + + self._check_data_profiles_same_length() + self.battery = Battery(capacity=battery_capacity, max_battery_charge_per_timestep=max_battery_charge_per_timestep) - assert len(self.solar_generation_profile) == len(self.electricity_load_profile), \ - "Solar generation profile and electricity load profile must be of the same length." - self.step_count = 0 self.start_index = 0 pass @@ -42,7 +51,7 @@ def __init__(self, def reset(self): """ - 1. Resetting the state of the building by calling `reset()` method from the building class. + 1. Resetting the state of the building by calling `reset()` method from the battery class. 2. Resetting the `step_count` to 0. The `step_count` is used for temporal orientation in the electricity load and solar generation profile. @@ -58,8 +67,7 @@ def simulate_one_step(self, action: float) -> Tuple[float, float]: 1. Charging or discharging the battery depending on the amount. 2. Calculating the amount of energy consumed by the building in this time step. 3. Trimming the amount of energy to 0, in case it is negative. - 4. Calculating the amount of excess energy which is considered lost. - 5. Increasing the step counter. + 4. Increasing the step counter. :param action: Fraction of energy to be stored or retrieved from the battery. The action lies in [-1;1]. The action represents the fraction of `max_battery_charge_per_timestep` which should be used to charge or @@ -69,22 +77,28 @@ def simulate_one_step(self, action: float) -> Tuple[float, float]: :returns: Tuple of: 1. Amount of energy consumed in this time step. This is calculated by: `battery_energy` - + `electricity_load` - `solar_generation`. Note that negative values are trimmed to 0. This means, that energy - can not be "gained". Excess energy from the solar energy system which is not used + + `electricity_load` - `solar_generation`. Note that negative values are trimmed to 0. This means, + no energy can be fed into the grid. Excess energy from the solar energy system which is not used to charge the battery is considered lost. Better use it to charge the battery ;-) - 2. Amount of excess energy which is considered lost. + 2. Electricity price in € cent per kWh. :rtype: (float, float) """ - electricity_load_of_this_timestep = self.electricity_load_profile[self.start_index + self.step_count] - solar_generation_of_this_timestep = self.solar_generation_profile[self.start_index + self.step_count] + electricity_load_this_timestep = self.electricity_load_profile[self.start_index + self.step_count] + solar_generation_this_timestep = self.solar_generation_profile[self.start_index + self.step_count] + electricity_price_this_timestep = self.electricity_price[self.start_index + self.step_count] electricity_consumed_for_battery = self.battery.use(action * self.battery.max_battery_charge_per_timestep) - electricity_consumption = electricity_consumed_for_battery + electricity_load_of_this_timestep - \ - solar_generation_of_this_timestep - excess_energy = 0 - if electricity_consumption < 0: - excess_energy = -1 * electricity_consumption - electricity_consumption = 0 + electricity_consumption_this_timestep = electricity_consumed_for_battery + electricity_load_this_timestep - \ + solar_generation_this_timestep + + if electricity_consumption_this_timestep < 0: + electricity_consumption_this_timestep = 0 self.step_count += 1 - return electricity_consumption, excess_energy + return electricity_consumption_this_timestep, electricity_price_this_timestep + + def _check_data_profiles_same_length(self): + if len(self.solar_generation_profile) == len(self.electricity_load_profile) == len(self.electricity_price): + pass + else: + raise ValueError('Data profiles passed to simulation are not of the same length.') diff --git a/building_energy_storage_simulation/data/data_preprocessing.ipynb b/building_energy_storage_simulation/data/data_preprocessing.ipynb index 45a1e07..b19f4ef 100644 --- a/building_energy_storage_simulation/data/data_preprocessing.ipynb +++ b/building_energy_storage_simulation/data/data_preprocessing.ipynb @@ -1,13 +1,5 @@ { "cells": [ - { - "cell_type": "markdown", - "id": "7a38795d", - "metadata": {}, - "source": [ - "The files processed in this notebook were obtained from [https://github.com/intelligent-environments-lab/CityLearn](https://github.com/intelligent-environments-lab/CityLearn)" - ] - }, { "cell_type": "code", "execution_count": null, @@ -19,6 +11,16 @@ "import matplotlib.pyplot as plt" ] }, + { + "cell_type": "markdown", + "id": "33a01875-61fa-4364-9114-8d02290100a5", + "metadata": {}, + "source": [ + "# Electricity Load and Solar Generation Data\n", + "\n", + "This data was obtained from [https://github.com/intelligent-environments-lab/CityLearn](https://github.com/intelligent-environments-lab/CityLearn)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -86,15 +88,48 @@ "solar_generation_profile.to_csv('preprocessed/solar_generation_profile.csv')" ] }, + { + "cell_type": "markdown", + "id": "f9ea2dad-74fb-40a3-a404-82c3dc672ab1", + "metadata": {}, + "source": [ + "# Price Data \n", + "\n", + "Data represents stock marked prices from Germany in 2022 taken from [Energy Charts](https://www.energy-charts.info/)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8ed67333-d1a5-4e45-b6b4-15df645893db", + "metadata": {}, + "outputs": [], + "source": [ + "electricity_price_profile = pd.read_csv('raw/energy-charts_Electricity_production_and_spot_prices_in_Germany_in_2022.csv')\n", + "\n", + "electricity_price_profile = electricity_price_profile['Day Ahead Auction'][1:]\n", + "electricity_price_profile = pd.to_numeric(electricity_price_profile)\n", + "electricity_price_profile = electricity_price_profile / 1000 * 100 # convert from MW to kW and from € to cents" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "79a1f7b4-e25c-4eb1-bdf9-d4c3e71a6570", + "metadata": {}, + "outputs": [], + "source": [ + "electricity_price_profile.to_csv('preprocessed/electricity_price_profile.csv')" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "d39a0d2f", + "id": "feeba086-0635-4f3d-ad07-3fdca3448bbc", "metadata": {}, "outputs": [], "source": [ - "weather_profile = pd.read_csv('raw/weather_data.csv')\n", - "weather_profile" + "pd.read_csv('preprocessed/electricity_price_profile.csv')" ] } ], @@ -114,7 +149,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/building_energy_storage_simulation/data/preprocessed/electricity_price_profile.csv b/building_energy_storage_simulation/data/preprocessed/electricity_price_profile.csv new file mode 100644 index 0000000..8ef6697 --- /dev/null +++ b/building_energy_storage_simulation/data/preprocessed/electricity_price_profile.csv @@ -0,0 +1,8761 @@ +,Day Ahead Auction +1,5.005 +2,4.133 +3,4.322 +4,4.546 +5,3.7670000000000003 +6,3.9700000000000006 +7,4.059 +8,4.326 +9,4.965999999999999 +10,7.005 +11,7.679000000000001 +12,8.41 +13,9.473999999999998 +14,9.68 +15,9.717 +16,10.100000000000001 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a/building_energy_storage_simulation/environment.py b/building_energy_storage_simulation/environment.py index 92cdba4..ca3caa5 100644 --- a/building_energy_storage_simulation/environment.py +++ b/building_energy_storage_simulation/environment.py @@ -35,7 +35,7 @@ def __init__(self, # Using np.inf as bounds as the observations must be rescaled externally anyways. E.g. Using the VecNormalize # wrapper from StableBaselines3 # (see https://stable-baselines.readthedocs.io/en/master/guide/vec_envs.html#vecnormalize) - self.observation_space = gym.spaces.Box(shape=(self.num_forecasting_steps * 2 + 1,), + self.observation_space = gym.spaces.Box(shape=(self.num_forecasting_steps * 3 + 1,), low=-np.inf, high=np.inf, dtype=np.float32) @@ -81,8 +81,8 @@ def step(self, action: ActType) -> Tuple[ObsType, float, bool, bool, dict]: 1. observation 2. reward 3. terminated. If true, the episode is over. - 4. truncated. Is always false, it is not implemented yet. - 5. Additional Information about the `electricity_comsumption` and the `excess_energy` of the current + 4. truncated. Is always false, as it is not implemented yet. + 5. Additional Information about the `electricity_consumption` and the `electricity_price` of the current time step :rtype: (observation, float, bool, bool, dict) @@ -90,28 +90,30 @@ def step(self, action: ActType) -> Tuple[ObsType, float, bool, bool, dict]: if hasattr(action, "__len__"): action = action[0] - electricity_consumption, excess_energy = self.building_simulation.simulate_one_step(action) - reward = Environment.calc_reward(electricity_consumption, excess_energy) + electricity_consumption, electricity_price = self.building_simulation.simulate_one_step(action) + reward = Environment.calc_reward(electricity_consumption, electricity_price) observation = self.get_observation() - return observation, reward, self.get_terminated(), False, {'electricity_consumption': electricity_consumption, - 'excess_energy': excess_energy} + return observation, reward, self._get_terminated(), False, {'electricity_consumption': electricity_consumption, + 'electricity_price': electricity_price} - def get_terminated(self): + def _get_terminated(self): if self.building_simulation.step_count > self.max_timesteps: return True return False def get_observation(self): current_index = self.building_simulation.start_index + self.building_simulation.step_count - electric_load_forecast = self.building_simulation.electricity_load_profile[current_index: current_index + - self.num_forecasting_steps] - solar_gen_forecast = self.building_simulation.solar_generation_profile[current_index: current_index + - self.num_forecasting_steps] + sim = self.building_simulation + electric_load_forecast = sim.electricity_load_profile[current_index: current_index + self.num_forecasting_steps] + solar_gen_forecast = sim.solar_generation_profile[current_index: current_index + self.num_forecasting_steps] + energy_price_forecast = sim.electricity_price[current_index: current_index + self.num_forecasting_steps] + return np.concatenate(([self.building_simulation.battery.state_of_charge], electric_load_forecast, - solar_gen_forecast), + solar_gen_forecast, + energy_price_forecast), axis=0) @staticmethod - def calc_reward(electricity_consumption, excess_energy): - return -1 * electricity_consumption + def calc_reward(electricity_consumption, electricity_price): + return -1 * electricity_consumption * electricity_price diff --git a/building_energy_storage_simulation/utils.py b/building_energy_storage_simulation/utils.py index b1081b4..9b641fd 100644 --- a/building_energy_storage_simulation/utils.py +++ b/building_energy_storage_simulation/utils.py @@ -2,6 +2,6 @@ import pandas as pd -def load_profile(filename, column_name): +def load_profile(filename: str, column_name: str) -> pd.Series: stream = pkg_resources.resource_stream(__name__, 'data/preprocessed/' + filename) - return pd.read_csv(stream)[column_name] \ No newline at end of file + return pd.read_csv(stream)[column_name] diff --git a/docs/source/guide/installation.rst b/docs/source/guide/installation.rst deleted file mode 100644 index ff1a5df..0000000 --- a/docs/source/guide/installation.rst +++ /dev/null @@ -1,41 +0,0 @@ -.. _usage: - -Usage -===== - -.. _installation: - -Install Stable Release ----------------------- - -To install the building-energy-storage-simulation with pip, execute: - -.. code-block:: console - - (.venv) $ pip install building-energy-storage-simulation - -Install Development Version ---------------------------- - -To contribute to this project, with support for running tests and building the documentation. - -.. code-block:: bash - - git clone https://github.com/tobirohrer/building-energy-storage-simulation.git && cd building-energy-storage-simulation - pip install -e .[docs,tests] - -Example Usage -------------- - -To use the building-energy-storage-simulation, first install it using pip: - -.. code-block:: python - - from building_energy_storage_simulation import Environment, BuildingSimulation - - simulation = BuildingSimulation() - env = Environment(building_simulation=simulation) - - env.reset() - env.step(1) - ... diff --git a/docs/source/guide/mdp_definition.rst b/docs/source/guide/mdp_definition.rst deleted file mode 100644 index 753316c..0000000 --- a/docs/source/guide/mdp_definition.rst +++ /dev/null @@ -1,55 +0,0 @@ -.. _task: - -Task Description -================ - -The simulation contains a building with an energy consumption profile attached to it. The resulting electricity demand is always automatically covered by - -- primarily using electricity generated by the solar energy system, -- and secondary by using electricity provided by the grid to cover the full demand. - -The simulated building has a battery which can be used to store energy. You can think of using the battery as shifting the electricity demand. By storing energy, you are increasing the energy demand of the building and by discharging/obtaining energy from the battery you are decreasing the energy demand of the building. **The task is to choose when to charge and when to discharge the battery.** Hereby, the goal is to utilize the battery so energy usage from the solar energy system is maximized and usage of the energy grid is minimized. Note, that excess energy from the solar energy system which is not used by the electricity load or used to charge the battery is considered lost. So better use the solar energy to charge the battery in this case ;-) - - -Action Space ------------- -====== === === -Action Min Max -====== === === -Charge -1 1 -====== === === - -The actions lie in the interval of [-1;1]. The action represents a fraction of the maximum energy which can be retrieved from the battery (or used to charge the battery) per time step. - -- 1 means maximum charging the battery. The maximum charge per time step is defined by the parameter `max_battery_charge_per_timestep`. -- -1 means maximum discharging the battery, meaning "gaining" electricity out of the battery -- 0 means don't charge or discharge - -Observation Space ------------------ -=========== ================================== === === -Index Observation Min Max -=========== ================================== === === -0 State of Charge (in kWh) 0 Max Battery Capacity -[1; n] Forecast Electric Load (in kWh) 0 Max Load in Profile -[n+1; 2*n] Forecast Solar Generation (in kWh) 0 Max Solar Generation in Profile -=========== ================================== === === - -The length of the observation depends on the length of the forecast used. By default, the simulation uses a forecast length of 4. This means 4 time steps of an electric load forecast and 4 time steps of a solar generation forecast are included in the observation. In addition to that, the information about the current state of charge of the battery is contained in the observation. - -The length of the forecast can be defined by setting the parameter `num_forecasting_steps` of the `Environment()`. - - -Reward ------- - -As our goal is to use as less energy as possible, the reward is defined by the energy consumed at every time step (times -1): - -$$ r_t = -1 * electricity\_consumed_t $$ - -It is important to note, that the term `electricity_consumed` cannot be negative. This means, excess energy from the solar energy system which is not consumed by the electricity load or by charing the battery is considered lost (`electricity_consumed` is 0 in this case). - -Episode Ends ------------- - -The episode ends if the `max_timesteps` of the `Environment()` are reached. diff --git a/docs/source/index.rst b/docs/source/index.rst index a337456..0304beb 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -12,18 +12,12 @@ represents a building with an energy storage (in form of a battery) and a solar the energy storage in such a way that the energy of the solar system can be used optimally. A more detailed description of the task itself and the description of the markov decision process, containing -information about the action space, observation space and reward can be found :ref:`here `. +information about the action space, observation space and reward can be found +`here `_. Contents -------- -.. toctree:: - :maxdepth: 1 - :Caption: User Guide - - guide/installation - guide/mdp_definition - .. toctree:: :maxdepth: 1 :Caption: API diff --git a/reinforcement_learning_sample_implementation.ipynb b/reinforcement_learning_sample_implementation.ipynb index 5c180a8..b326323 100644 --- a/reinforcement_learning_sample_implementation.ipynb +++ b/reinforcement_learning_sample_implementation.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -38,6 +38,16 @@ "# Create Environment\n", "sim = BuildingSimulation()\n", "env = Environment(sim)\n", + "initial_obs, info = env.reset()\n", + "print(initial_obs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "# Wrap with Monitor() so a log of the training is saved \n", "env = Monitor(env, filename=RL_PATH)\n", "# Warp with DummyVecEnc() so the observations and reward can be normalized using VecNormalize()\n", @@ -47,555 +57,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using cpu device\n", - "----------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 2e+03 |\n", - "| ep_rew_mean | -5.15e+04 |\n", - "| time/ | |\n", - "| fps | 1850 |\n", - "| iterations | 1 |\n", - "| time_elapsed | 1 |\n", - "| total_timesteps | 2048 |\n", - "----------------------------------\n", - "------------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 2e+03 |\n", - "| ep_rew_mean | -5.15e+04 |\n", - "| time/ | |\n", - "| fps | 1381 |\n", - "| iterations | 2 |\n", - "| time_elapsed | 2 |\n", - "| total_timesteps | 4096 |\n", - "| train/ | |\n", - "| approx_kl | 0.0058020703 |\n", - "| clip_fraction | 0.0352 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.43 |\n", - "| explained_variance | -0.704 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | 0.105 |\n", - "| n_updates | 10 |\n", - "| policy_gradient_loss | -0.00382 |\n", - "| std | 1.01 |\n", - "| value_loss | 0.268 |\n", - "------------------------------------------\n", - "-----------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 2e+03 |\n", - "| ep_rew_mean | -5.18e+04 |\n", - "| time/ | |\n", - "| fps | 1270 |\n", - "| iterations | 3 |\n", - "| time_elapsed | 4 |\n", - "| total_timesteps | 6144 |\n", - "| train/ | |\n", - "| approx_kl | 0.004206354 |\n", - "| clip_fraction | 0.0407 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.42 |\n", - "| explained_variance | 0.53 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | 0.0239 |\n", - "| n_updates | 20 |\n", - "| policy_gradient_loss | -0.00509 |\n", - "| std | 1 |\n", - "| value_loss | 0.0233 |\n", - "-----------------------------------------\n", - "-----------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 2e+03 |\n", - "| ep_rew_mean | -5.18e+04 |\n", - "| time/ | |\n", - "| fps | 1191 |\n", - "| iterations | 4 |\n", - "| time_elapsed | 6 |\n", - "| total_timesteps | 8192 |\n", - "| train/ | |\n", - "| approx_kl | 0.003434807 |\n", - "| clip_fraction | 0.0254 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.41 |\n", - "| explained_variance | 0.654 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | 0.0054 |\n", - "| n_updates | 30 |\n", - "| policy_gradient_loss | -0.00232 |\n", - "| std | 0.988 |\n", - "| value_loss | 0.026 |\n", - "-----------------------------------------\n", - "-----------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 2e+03 |\n", - "| ep_rew_mean | -5.19e+04 |\n", - "| time/ | |\n", - "| fps | 1133 |\n", - "| iterations | 5 |\n", - "| time_elapsed | 9 |\n", - "| total_timesteps | 10240 |\n", - "| train/ | |\n", - "| approx_kl | 0.004454828 |\n", - "| clip_fraction | 0.0244 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.4 |\n", - "| explained_variance | 0.679 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | 0.0189 |\n", - "| n_updates | 40 |\n", - "| policy_gradient_loss | -0.00288 |\n", - "| std | 0.969 |\n", - "| value_loss | 0.0251 |\n", - "-----------------------------------------\n", - "------------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 2e+03 |\n", - "| ep_rew_mean | -5.18e+04 |\n", - "| time/ | |\n", - "| fps | 1095 |\n", - "| iterations | 6 |\n", - "| time_elapsed | 11 |\n", - "| total_timesteps | 12288 |\n", - "| train/ | |\n", - "| approx_kl | 0.0048774686 |\n", - "| clip_fraction | 0.0361 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.38 |\n", - "| explained_variance | 0.692 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | 0.0108 |\n", - "| n_updates | 50 |\n", - "| policy_gradient_loss | -0.00361 |\n", - "| std | 0.954 |\n", - "| value_loss | 0.0254 |\n", - "------------------------------------------\n", - "-----------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 2e+03 |\n", - "| ep_rew_mean | -5.16e+04 |\n", - "| time/ | |\n", - "| fps | 1085 |\n", - "| iterations | 7 |\n", - "| time_elapsed | 13 |\n", - "| total_timesteps | 14336 |\n", - "| train/ | |\n", - "| approx_kl | 0.006036303 |\n", - "| clip_fraction | 0.055 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.37 |\n", - "| explained_variance | 0.71 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | 0.021 |\n", - "| n_updates | 60 |\n", - "| policy_gradient_loss | -0.00378 |\n", - "| std | 0.958 |\n", - "| value_loss | 0.0237 |\n", - "-----------------------------------------\n", - "------------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 2e+03 |\n", - "| ep_rew_mean | -5.14e+04 |\n", - "| time/ | |\n", - "| fps | 1085 |\n", - "| iterations | 8 |\n", - "| time_elapsed | 15 |\n", - "| total_timesteps | 16384 |\n", - "| train/ | |\n", - "| approx_kl | 0.0030021344 |\n", - "| clip_fraction | 0.0276 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.38 |\n", - "| explained_variance | 0.7 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | 0.00112 |\n", - "| n_updates | 70 |\n", - "| policy_gradient_loss | -0.00212 |\n", - "| std | 0.964 |\n", - "| value_loss | 0.0235 |\n", - "------------------------------------------\n", - "------------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 2e+03 |\n", - "| ep_rew_mean | -5.13e+04 |\n", - "| time/ | |\n", - "| fps | 1080 |\n", - "| iterations | 9 |\n", - "| time_elapsed | 17 |\n", - "| total_timesteps | 18432 |\n", - "| train/ | |\n", - "| approx_kl | 0.0033348883 |\n", - "| clip_fraction | 0.0368 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.38 |\n", - "| explained_variance | 0.706 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | 0.00327 |\n", - "| n_updates | 80 |\n", - "| policy_gradient_loss | -0.00371 |\n", - "| std | 0.964 |\n", - "| value_loss | 0.0241 |\n", - "------------------------------------------\n", - "-----------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 2e+03 |\n", - "| ep_rew_mean | -5.11e+04 |\n", - "| time/ | |\n", - "| fps | 1075 |\n", - "| iterations | 10 |\n", - "| time_elapsed | 19 |\n", - "| total_timesteps | 20480 |\n", - "| train/ | |\n", - "| approx_kl | 0.004695772 |\n", - "| clip_fraction | 0.0353 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.38 |\n", - "| explained_variance | 0.712 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | 0.00686 |\n", - "| n_updates | 90 |\n", - "| policy_gradient_loss | -0.0031 |\n", - "| std | 0.96 |\n", - "| value_loss | 0.0249 |\n", - "-----------------------------------------\n", - "----------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 2e+03 |\n", - "| ep_rew_mean | -5.1e+04 |\n", - "| time/ | |\n", - "| fps | 1072 |\n", - "| iterations | 11 |\n", - "| time_elapsed | 20 |\n", - "| total_timesteps | 22528 |\n", - "| train/ | |\n", - "| approx_kl | 0.00467043 |\n", - "| clip_fraction | 0.0364 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.38 |\n", - "| explained_variance | 0.716 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | 0.039 |\n", - "| n_updates | 100 |\n", - "| policy_gradient_loss | -0.00291 |\n", - "| std | 0.97 |\n", - "| value_loss | 0.0246 |\n", - "----------------------------------------\n", - "-----------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 2e+03 |\n", - "| ep_rew_mean | -5.09e+04 |\n", - "| time/ | |\n", - "| fps | 1064 |\n", - "| iterations | 12 |\n", - "| time_elapsed | 23 |\n", - "| total_timesteps | 24576 |\n", - "| train/ | |\n", - "| approx_kl | 0.004140622 |\n", - "| clip_fraction | 0.0323 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.38 |\n", - "| explained_variance | 0.72 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | 0.0196 |\n", - "| n_updates | 110 |\n", - "| policy_gradient_loss | -0.00329 |\n", - "| std | 0.962 |\n", - "| value_loss | 0.0254 |\n", - "-----------------------------------------\n", - "-----------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 2e+03 |\n", - "| ep_rew_mean | -5.08e+04 |\n", - "| time/ | |\n", - "| fps | 1064 |\n", - "| iterations | 13 |\n", - "| time_elapsed | 25 |\n", - "| total_timesteps | 26624 |\n", - "| train/ | |\n", - "| approx_kl | 0.004255478 |\n", - "| clip_fraction | 0.0271 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.38 |\n", - "| explained_variance | 0.704 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | 0.0184 |\n", - "| n_updates | 120 |\n", - "| policy_gradient_loss | -0.00361 |\n", - "| std | 0.96 |\n", - "| value_loss | 0.0248 |\n", - "-----------------------------------------\n", - "----------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 2e+03 |\n", - "| ep_rew_mean | -5.07e+04 |\n", - "| time/ | |\n", - "| fps | 1062 |\n", - "| iterations | 14 |\n", - "| time_elapsed | 26 |\n", - "| total_timesteps | 28672 |\n", - "| train/ | |\n", - "| approx_kl | 0.00420307 |\n", - "| clip_fraction | 0.0248 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.37 |\n", - "| explained_variance | 0.715 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | 0.00972 |\n", - "| n_updates | 130 |\n", - "| policy_gradient_loss | -0.00254 |\n", - "| std | 0.951 |\n", - "| value_loss | 0.0237 |\n", - "----------------------------------------\n", - "------------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 2e+03 |\n", - "| ep_rew_mean | -5.07e+04 |\n", - "| time/ | |\n", - "| fps | 1055 |\n", - "| iterations | 15 |\n", - "| time_elapsed | 29 |\n", - "| total_timesteps | 30720 |\n", - "| train/ | |\n", - "| approx_kl | 0.0036043213 |\n", - "| clip_fraction | 0.0288 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.36 |\n", - "| explained_variance | 0.722 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | 0.00327 |\n", - "| n_updates | 140 |\n", - "| policy_gradient_loss | -0.00277 |\n", - "| std | 0.937 |\n", - "| value_loss | 0.026 |\n", - "------------------------------------------\n", - "-----------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 2e+03 |\n", - "| ep_rew_mean | -5.05e+04 |\n", - "| time/ | |\n", - "| fps | 1050 |\n", - "| iterations | 16 |\n", - "| time_elapsed | 31 |\n", - "| total_timesteps | 32768 |\n", - "| train/ | |\n", - "| approx_kl | 0.006608868 |\n", - "| clip_fraction | 0.0544 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.35 |\n", - "| explained_variance | 0.713 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | 0.0205 |\n", - "| n_updates | 150 |\n", - "| policy_gradient_loss | -0.00405 |\n", - "| std | 0.933 |\n", - "| value_loss | 0.0268 |\n", - "-----------------------------------------\n", - "------------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 2e+03 |\n", - "| ep_rew_mean | -5.04e+04 |\n", - "| time/ | |\n", - "| fps | 1049 |\n", - "| iterations | 17 |\n", - "| time_elapsed | 33 |\n", - "| total_timesteps | 34816 |\n", - "| train/ | |\n", - "| approx_kl | 0.0061956686 |\n", - "| clip_fraction | 0.0531 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.34 |\n", - "| explained_variance | 0.711 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | 0.0241 |\n", - "| n_updates | 160 |\n", - "| policy_gradient_loss | -0.00386 |\n", - "| std | 0.919 |\n", - "| value_loss | 0.0254 |\n", - "------------------------------------------\n", - "------------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 2e+03 |\n", - "| ep_rew_mean | -5.03e+04 |\n", - "| time/ | |\n", - "| fps | 1043 |\n", - "| iterations | 18 |\n", - "| time_elapsed | 35 |\n", - "| total_timesteps | 36864 |\n", - "| train/ | |\n", - "| approx_kl | 0.0029415619 |\n", - "| clip_fraction | 0.0137 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.33 |\n", - "| explained_variance | 0.731 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | 0.0135 |\n", - "| n_updates | 170 |\n", - "| policy_gradient_loss | -0.000395 |\n", - "| std | 0.911 |\n", - "| value_loss | 0.0247 |\n", - "------------------------------------------\n", - "------------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 2e+03 |\n", - "| ep_rew_mean | -5.02e+04 |\n", - "| time/ | |\n", - "| fps | 1032 |\n", - "| iterations | 19 |\n", - "| time_elapsed | 37 |\n", - "| total_timesteps | 38912 |\n", - "| train/ | |\n", - "| approx_kl | 0.0062524695 |\n", - "| clip_fraction | 0.0591 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.32 |\n", - "| explained_variance | 0.724 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | 0.00226 |\n", - "| n_updates | 180 |\n", - "| policy_gradient_loss | -0.00384 |\n", - "| std | 0.899 |\n", - "| value_loss | 0.0242 |\n", - "------------------------------------------\n", - "------------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 2e+03 |\n", - "| ep_rew_mean | -5.01e+04 |\n", - "| time/ | |\n", - "| fps | 1016 |\n", - "| iterations | 20 |\n", - "| time_elapsed | 40 |\n", - "| total_timesteps | 40960 |\n", - "| train/ | |\n", - "| approx_kl | 0.0021706955 |\n", - "| clip_fraction | 0.0165 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.31 |\n", - "| explained_variance | 0.728 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | 0.00736 |\n", - "| n_updates | 190 |\n", - "| policy_gradient_loss | -0.000648 |\n", - "| std | 0.895 |\n", - "| value_loss | 0.0234 |\n", - "------------------------------------------\n", - "-----------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 2e+03 |\n", - "| ep_rew_mean | -5e+04 |\n", - "| time/ | |\n", - "| fps | 995 |\n", - "| iterations | 21 |\n", - "| time_elapsed | 43 |\n", - "| total_timesteps | 43008 |\n", - "| train/ | |\n", - "| approx_kl | 0.004316492 |\n", - "| clip_fraction | 0.0293 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.31 |\n", - "| explained_variance | 0.725 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | -0.00454 |\n", - "| n_updates | 200 |\n", - "| policy_gradient_loss | -0.00292 |\n", - "| std | 0.895 |\n", - "| value_loss | 0.0247 |\n", - "-----------------------------------------\n", - "------------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 2e+03 |\n", - "| ep_rew_mean | -4.99e+04 |\n", - "| time/ | |\n", - "| fps | 974 |\n", - "| iterations | 22 |\n", - "| time_elapsed | 46 |\n", - "| total_timesteps | 45056 |\n", - "| train/ | |\n", - "| approx_kl | 0.0033846092 |\n", - "| clip_fraction | 0.0414 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.31 |\n", - "| explained_variance | 0.725 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | -0.00558 |\n", - "| n_updates | 210 |\n", - "| policy_gradient_loss | -0.00289 |\n", - "| std | 0.895 |\n", - "| value_loss | 0.0247 |\n", - "------------------------------------------\n", - "-----------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 2e+03 |\n", - "| ep_rew_mean | -4.98e+04 |\n", - "| time/ | |\n", - "| fps | 956 |\n", - "| iterations | 23 |\n", - "| time_elapsed | 49 |\n", - "| total_timesteps | 47104 |\n", - "| train/ | |\n", - "| approx_kl | 0.005007515 |\n", - "| clip_fraction | 0.0445 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.3 |\n", - "| explained_variance | 0.717 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | 0.0147 |\n", - "| n_updates | 220 |\n", - "| policy_gradient_loss | -0.00462 |\n", - "| std | 0.887 |\n", - "| value_loss | 0.0242 |\n", - "-----------------------------------------\n", - "------------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 2e+03 |\n", - "| ep_rew_mean | -4.97e+04 |\n", - "| time/ | |\n", - "| fps | 932 |\n", - "| iterations | 24 |\n", - "| time_elapsed | 52 |\n", - "| total_timesteps | 49152 |\n", - "| train/ | |\n", - "| approx_kl | 0.0040945145 |\n", - "| clip_fraction | 0.0554 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.29 |\n", - "| explained_variance | 0.729 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | -0.00793 |\n", - "| n_updates | 230 |\n", - "| policy_gradient_loss | -0.00442 |\n", - "| std | 0.878 |\n", - "| value_loss | 0.0245 |\n", - "------------------------------------------\n", - "-----------------------------------------\n", - "| rollout/ | |\n", - "| ep_len_mean | 2e+03 |\n", - "| ep_rew_mean | -4.96e+04 |\n", - "| time/ | |\n", - "| fps | 908 |\n", - "| iterations | 25 |\n", - "| time_elapsed | 56 |\n", - "| total_timesteps | 51200 |\n", - "| train/ | |\n", - "| approx_kl | 0.003998294 |\n", - "| clip_fraction | 0.0385 |\n", - "| clip_range | 0.2 |\n", - "| entropy_loss | -1.29 |\n", - "| explained_variance | 0.731 |\n", - "| learning_rate | 0.0003 |\n", - "| loss | -0.0039 |\n", - "| n_updates | 240 |\n", - "| policy_gradient_loss | -0.00351 |\n", - "| std | 0.874 |\n", - "| value_loss | 0.024 |\n", - "-----------------------------------------\n" - ] - } - ], + "outputs": [], "source": [ "# Train with PPO :-)\n", "model = PPO(\"MlpPolicy\", env, verbose=1, gamma=0.95)\n", @@ -614,30 +78,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plot the training process\n", "training_log = pd.read_csv(RL_PATH + 'monitor.csv', skiprows=1)\n", @@ -646,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -664,44 +107,23 @@ " original_obs = env.get_original_obs()[0]\n", " observations.append(original_obs)\n", " electricity_consumption.append(info[0]['electricity_consumption'])\n", - " excess_energy.append(info[0]['excess_energy'])\n", + " excess_energy.append(info[0]['electricity_price'])\n", " rewards.append(r)\n", " \n", "trajectory = pd.DataFrame({\n", " 'action': actions,\n", " 'observations': observations,\n", " 'electricity_consumption': electricity_consumption,\n", - " 'excess_energy': excess_energy,\n", + " 'electricity_price': excess_energy,\n", " 'reward': rewards\n", " }) " ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plot_data = trajectory[0:200]\n", "observation_df = plot_data['observations'].apply(pd.Series)\n", @@ -709,11 +131,9 @@ "plt.rcParams[\"figure.figsize\"] = (16,10)\n", "\n", "fig, ax = plt.subplots()\n", - "#ax.plot(observation_df[0], label = 'state of charge')\n", "ax.plot(observation_df[1], label = 'electric load')\n", "ax.plot(observation_df[5], label = 'solar generation')\n", - "#ax.plot(plot_data['excess_energy'], label = 'excess energy')\n", - "#ax.plot(plot_data['electricity_consumption'], label = 'electricity consumption')\n", + "ax.plot(plot_data['electricity_price'], label = 'electricity_price')\n", "\n", "ax1 = ax.twinx()\n", "ax1.plot(plot_data['action'], label = 'action', color = 'black')\n", @@ -729,62 +149,51 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "env.training = False\n", "\n", - "electricity_consumption = []\n", + "cost = []\n", "done = False\n", "obs = env.reset()\n", "while not done:\n", " action = model.predict(obs, deterministic=True)\n", " obs, r, done, info = env.step([action[0][0]])\n", - " electricity_consumption.append(info[0]['electricity_consumption'])\n", + " cost.append(info[0]['electricity_consumption'] * info[0]['electricity_price'])\n", "\n", - "electricity_consumption = sum(electricity_consumption)" + "cost = sum(cost)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "env.training = False\n", "\n", - "baseline_electricity_consumption = []\n", + "baseline_cost = []\n", "done = False\n", "obs = env.reset()\n", "while not done:\n", " # Always taking noop as action. This is the electricity demand if there would be no battery\n", " action = [0]\n", " obs, r, done, info = env.step(action)\n", - " baseline_electricity_consumption.append(info[0]['electricity_consumption'])\n", + " baseline_cost.append(info[0]['electricity_consumption'] * info[0]['electricity_price'])\n", "\n", - "baseline_electricity_consumption = sum(baseline_electricity_consumption)" + "baseline_cost = sum(baseline_cost)" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9376004896108261" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# how much energy did we save by utilizing the battery?\n", - "electricity_consumption / baseline_electricity_consumption" + "cost / baseline_cost" ] } ], @@ -804,7 +213,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/setup.py b/setup.py index 82d075f..56a2db8 100644 --- a/setup.py +++ b/setup.py @@ -4,7 +4,7 @@ long_description = (this_directory / "README.md").read_text() setup(name='building_energy_storage_simulation', - version='0.7.0', + version='0.8.0', description='A simulation of a building to optimize energy storage utilization.', long_description=long_description, long_description_content_type='text/markdown', diff --git a/tests/test_building_simulation.py b/tests/test_building_simulation.py index 8a2c879..87a202a 100644 --- a/tests/test_building_simulation.py +++ b/tests/test_building_simulation.py @@ -1,3 +1,5 @@ +import pytest + from building_energy_storage_simulation import BuildingSimulation @@ -13,3 +15,20 @@ def test_simulation_reads_default_data_profiles(): sim = BuildingSimulation() assert len(sim.electricity_load_profile) == 8760 assert len(sim.solar_generation_profile) == 8760 + + +@pytest.mark.parametrize( + "electricity_price", [1, 1.1] +) +def test_simulation_scalar_electricity_price_converted_into_profile(electricity_price): + sim = BuildingSimulation(electricity_load_profile=[0, 0, 0], + solar_generation_profile=[0, 0, 0], + electricity_price=electricity_price) + assert len(sim.electricity_price) == 3 + + +def test_simulation_throws_when_data_profiles_have_unequal_length(): + with pytest.raises(Exception) as exception_info: + sim = BuildingSimulation(electricity_load_profile=[0, 0, 0], + solar_generation_profile=[0, 0]) + assert exception_info.errisinstance(ValueError) diff --git a/tests/test_environment.py b/tests/test_environment.py index 9bd50ba..479af84 100644 --- a/tests/test_environment.py +++ b/tests/test_environment.py @@ -34,7 +34,7 @@ def test_observation_size(building_simulation): env = Environment(building_simulation=building_simulation, max_timesteps=5, num_forecasting_steps=4) initial_obs, info = env.reset() obs, reward, terminated, trunc, info = env.step(0) - assert len(obs) == 9 + assert len(obs) == 13 def test_initial_obs_step_obs_same_size(building_simulation): From 146e2de9f7ee7df12fb868b7d085fcdc26503a71 Mon Sep 17 00:00:00 2001 From: tobirohrer Date: Sun, 8 Oct 2023 13:45:55 +0200 Subject: [PATCH 2/4] Fixing tests --- tests/test_building_simulation.py | 2 +- tests/test_environment.py | 9 +++++++++ 2 files changed, 10 insertions(+), 1 deletion(-) diff --git a/tests/test_building_simulation.py b/tests/test_building_simulation.py index 87a202a..dd8fd15 100644 --- a/tests/test_building_simulation.py +++ b/tests/test_building_simulation.py @@ -4,7 +4,7 @@ def test_energy_consumption_is_trimmed_to_0(): - sim = BuildingSimulation(electricity_load_profile=[0], solar_generation_profile=[100]) + sim = BuildingSimulation(electricity_load_profile=[0], solar_generation_profile=[100], electricity_price=[1]) # Don´t charge the battery, meaning don´t do anything with the 100kWh energy we gained from the solar system. electricity_consumption, excess_energy = sim.simulate_one_step(0) # Still the consumption is 0, as we loose excess electricity which we do not use to charge the battery. diff --git a/tests/test_environment.py b/tests/test_environment.py index 479af84..fc77027 100644 --- a/tests/test_environment.py +++ b/tests/test_environment.py @@ -8,6 +8,7 @@ def building_simulation(): return BuildingSimulation(electricity_load_profile=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10], solar_generation_profile=[10, 9, 8, 7, 6, 5, 4, 3, 2, 1], + electricity_price=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10], max_battery_charge_per_timestep=20) @@ -60,3 +61,11 @@ def test_reset(building_simulation): env.reset() assert env.building_simulation.step_count == 0 assert env.building_simulation.battery.state_of_charge == 0 + + +def test_default_initialization_runs_without_throwing(): + sim = BuildingSimulation() + env = Environment(sim) + env.reset() + env.step(1) + assert env.building_simulation.step_count == 1 From 4c65d521029ac0c4ee144b5f4e5b47b38b7a2314 Mon Sep 17 00:00:00 2001 From: Tobias Rohrer Date: Sun, 8 Oct 2023 13:52:51 +0200 Subject: [PATCH 3/4] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 9d3c25d..a802c4e 100644 --- a/README.md +++ b/README.md @@ -92,7 +92,7 @@ The actions lie in the interval of [-1;1]. The action represents a fraction of t | [2n+1; 3*n] | Electricity Price (in € cent per kWh) | Min of Price Profile | Max of Price Profile | -The length of the observation depends on the length of the forecast used. By default, the simulation uses a forecast length of 4. +The length of the observation depends on the length of the forecast ($n$) used. By default, the simulation uses a forecast length of 4. This means 4 time steps of an electric load forecast, 4 time steps of a solar generation forecast and 4 time steps of the electric price profile are included in the observation. In addition to that, the information about the current state of charge of the battery is contained in the observation. From 0c3c53cb2cb36a010e821ebd0718fbb21dd24edb Mon Sep 17 00:00:00 2001 From: Tobias Rohrer Date: Sun, 8 Oct 2023 13:53:36 +0200 Subject: [PATCH 4/4] Update README.md --- README.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index a802c4e..8d53028 100644 --- a/README.md +++ b/README.md @@ -26,13 +26,6 @@ git clone https://github.com/tobirohrer/building-energy-storage-simulation.git & pip install -e .[docs,tests] ``` -## Contribute & Contact - -As I just started with this project, I am very happy for any kind of -contribution! In case you want to contribute, or if you have any -questions, contact me via -[discord](https://discord.com/users/tobirohrer#8654). - ## Usage ```python @@ -108,3 +101,10 @@ The episode ends if the `max_timesteps` of the `Environment()` are reached. The documentation is available at [https://building-energy-storage-simulation.readthedocs.io/](https://building-energy-storage-simulation.readthedocs.io/) +## Contribute & Contact + +As I just started with this project, I am very happy for any kind of +contribution! In case you want to contribute, or if you have any +questions, contact me via +[discord](https://discord.com/users/tobirohrer#8654). +