Renewable Energy Shortfall Modeling Project Readme Project Overview The aim of this project is to model the daily energy shortfall between the energy generated by fossil fuels and various renewable sources in Spain. The daily shortfall, which is the target variable, will be modeled as a function of various city-specific weather features such as pressure, wind speed, humidity, and more.
Project Team Team Leader: Christelle Coetzee Project Manager: Mashako Justice Manyelo Team Members: Destiny Owobu Edidiong Udofia Anthonia Omonayin Pricilla Vhafuniwap
Dataset Data Source: The dataset contains historical data related to energy generation and weather features for various cities in Spain. Data Columns time: Timestamp for the data entry.
City-specific weather features:
Madrid_wind_speed Valencia_wind_deg Bilbao_rain_1h Valencia_wind_speed Seville_humidity Madrid_humidity Bilbao_clouds_all Bilbao_wind_speed Seville_clouds_all Bilbao_wind_deg Barcelona_wind_speed Barcelona_wind_deg Madrid_clouds_all Seville_wind_speed Barcelona_rain_1h Seville_pressure Seville_rain_1h Bilbao_snow_3h Barcelona_pressure Seville_rain_3h Madrid_rain_1h Barcelona_rain_3h Valencia_snow_3h Madrid_weather_id Barcelona_weather_id Bilbao_pressure Seville_weather_id Valencia_pressure Seville_temp_max Madrid_pressure Valencia_temp_max Valencia_temp Bilbao_weather_id Seville_temp Valencia_humidity Valencia_temp_min Barcelona_temp_max Madrid_temp_max Barcelona_temp Bilbao_temp_min Bilbao_temp Barcelona_temp_min Bilbao_temp_max Seville_temp_min Madrid_temp Madrid_temp_min load_shortfall_3h: The daily energy shortfall, which is the target variable.
Project Objective The primary objective of this project is to develop a regression model that predicts the daily energy shortfall in Spain based on the provided weather and city-specific data.
Project Deliverables Data Preprocessing:
Clean and preprocess the dataset, handling missing values, and encoding categorical variables as necessary.
Exploratory Data Analysis (EDA):
Conduct EDA to understand the relationships between weather features, energy generation, and the energy shortfall.
Feature Engineering:
Create new features if necessary and select relevant features for modeling.
Model Building:
Develop and train a regression model to predict the daily energy shortfall using the selected features.
Model Evaluation:
Evaluate the model's performance using appropriate metrics such as mean squared error, mean absolute error, and R-squared.
Model Deployment:
Deploy the trained model for making predictions on new data.
Documentation:
Create documentation that explains the data, methodology, and model for future reference.
Project Timeline
The project will be conducted in phases, with each phase having specific tasks and deadlines. The timeline for the project will be determined by the project manager in consultation with the team members.
Data Volume
The dataset contains nearly 8762 rows, and its size may impact data processing and model training times. Appropriate hardware and software resources will be allocated to handle this volume effectively.
Collaboration The project team will collaborate closely to ensure the successful completion of each phase. Communication channels will be established to facilitate teamwork and knowledge sharing.
Conclusion
The successful modeling of the daily energy shortfall in Spain will provide valuable insights for the government's renewable energy infrastructure investments, contributing to a more sustainable and reliable energy supply for the citizens.
For any inquiries or updates related to this project, please feel free to reach out to the following team members:
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Project Manager: Mashako Justice Manyelo
- Email: [email protected]
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Team Leader: Christelle Coetzee
- Email: [email protected]
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Team Members:
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Destiny Owobu
- Email: [email protected]
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Edidiong Udofia
- Email: [email protected]
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Tony Onyeka
- Email: [email protected]
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Pricilla Vhafuniwap
- Email: [email protected]
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You can also follow the progress of this project on our Trello board:
Feel free to contact us if you have any questions, suggestions, or updates related to this project.