Authors:
Sabereh Rastkar
;
Danial Zendehdel
;
Enrico De Santis
and
Antonello Rizzi
Affiliation:
Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, 00185 Roma, Italy
Keyword(s):
Time Series Energy Forecasting, Forecasting Algorithm, Seasonality Effect.
Abstract:
Accurate energy consumption forecasting is essential for optimizing resource allocation and ensuring a reliable energy supply. This paper conducts a thorough analysis of energy consumption forecasting using XGBoost, SARIMA, LSTM, and Seasonal-LSTM algorithms. It utilizes two years of hourly electricity demand data from Italy and the PJM region (USA), categorizing algorithms into seasonality and non-seasonality groups. Performance metrics like RMSE, MAE, R 2 , and MSPE are employed. The study underscores the importance of considering seasonality, with SARIMA and Seasonal-LSTM achieving high accuracy in the seasonality group. In the non-seasonality group, XGBoost and LSTM perform competitively. In summary, this research aids in choosing suitable forecasting algorithms for building an Energy Management System for smart energy management in microgrids, considering seasonality and data attributes. These insights can also benefit energy companies in efficient resource management, promoting
sustainable energy practices and urban development.
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