Recent years have seen an increasing interest in developing robust, accurate and possibly fast forecasting methods for both energy production and consumption. Traditional approaches based on linear architectures are not able to fully model the relationships between variables, particularly when dealing with many features. We propose a Gradient-Boosting–Machine-based framework to forecast the demand of mixed customers of an energy dispatching company, aggregated according to their location within the seven Italian electricity market zones. The main challenge is to provide precise one-day-ahead predictions, despite the most recent data being two months old. This requires exogenous regressors, e.g., as historical features of part of the customers and air temperature, to be incorporated in the scheme and tailored to the specific case. Numerical simulations are conducted, resulting in a MAPE of 5–15% according to the market zone. The Gradient Boosting performs significantly better when compared to classical statistical models for time series, such as ARMA, unable to capture holidays.

Energy Consumption Forecasts by Gradient Boosting Regression Trees

Luca Di Persio;Nicola Fraccarolo
2023-01-01

Abstract

Recent years have seen an increasing interest in developing robust, accurate and possibly fast forecasting methods for both energy production and consumption. Traditional approaches based on linear architectures are not able to fully model the relationships between variables, particularly when dealing with many features. We propose a Gradient-Boosting–Machine-based framework to forecast the demand of mixed customers of an energy dispatching company, aggregated according to their location within the seven Italian electricity market zones. The main challenge is to provide precise one-day-ahead predictions, despite the most recent data being two months old. This requires exogenous regressors, e.g., as historical features of part of the customers and air temperature, to be incorporated in the scheme and tailored to the specific case. Numerical simulations are conducted, resulting in a MAPE of 5–15% according to the market zone. The Gradient Boosting performs significantly better when compared to classical statistical models for time series, such as ARMA, unable to capture holidays.
2023
energy forecasting, machine learning, neural networks, Italian energy market, gradient boosting decision tree
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/1086646
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