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A Comparison Between Seasonal and Non-Seasonal Forecasting Techniques for Energy Demand Time Series in Smart Grids

Topics: Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World Applications, Financial Applications, Neural Prostheses and Medical Applications, Neural Based Data Mining and Complex Information Process; Neural based Implementation, Applications and Solutions; Stochastic Learning and Statistical Algorithms

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. (More)

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Paper citation in several formats:
Rastkar, S.; Zendehdel, D.; De Santis, E. and Rizzi, A. (2023). A Comparison Between Seasonal and Non-Seasonal Forecasting Techniques for Energy Demand Time Series in Smart Grids. In Proceedings of the 15th International Joint Conference on Computational Intelligence - NCTA; ISBN 978-989-758-674-3; ISSN 2184-3236, SciTePress, pages 459-467. DOI: 10.5220/0012265900003595

@conference{ncta23,
author={Sabereh Rastkar. and Danial Zendehdel. and Enrico {De Santis}. and Antonello Rizzi.},
title={A Comparison Between Seasonal and Non-Seasonal Forecasting Techniques for Energy Demand Time Series in Smart Grids},
booktitle={Proceedings of the 15th International Joint Conference on Computational Intelligence - NCTA},
year={2023},
pages={459-467},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012265900003595},
isbn={978-989-758-674-3},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computational Intelligence - NCTA
TI - A Comparison Between Seasonal and Non-Seasonal Forecasting Techniques for Energy Demand Time Series in Smart Grids
SN - 978-989-758-674-3
IS - 2184-3236
AU - Rastkar, S.
AU - Zendehdel, D.
AU - De Santis, E.
AU - Rizzi, A.
PY - 2023
SP - 459
EP - 467
DO - 10.5220/0012265900003595
PB - SciTePress