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Publicação
Short-Term Electricity Demand Forecasting with Machine Learning
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Autores
Orientador(es)
Resumo(s)
An accurate short-term load forecasting (STLF) is one of the most critical inputs for power plant units’
planning commitment. STLF reduces the overall planning uncertainty added by the intermittent
production of renewable sources; thus, it helps to minimize the hydro-thermal electricity production
costs in a power grid. Although there is some research in the field and even several research
applications, there is a continual need to improve forecasts. This project proposes a set of machine
learning (ML) models to improve the accuracy of 168 hours forecasts. The developed models employ
features from multiple sources, such as historical load, weather, and holidays. Of the five ML models
developed and tested in various load profile contexts, the Extreme Gradient Boosting Regressor
(XGBoost) algorithm showed the best results, surpassing previous historical weekly predictions based
on neural networks. Additionally, because XGBoost models are based on an ensemble of decision
trees, it facilitated the model’s interpretation, which provided a relevant additional result, the
features’ importance in the forecasting.
Descrição
Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
Palavras-chave
Short-Term Load Forecasting; Machine Learning; Weekly forecast; Electricity market; Extreme Gradient Boosting Regressor (XGBoost)