Intelligent Short-Term Hybrid Forecasting Model Applied on a Community-based Home Energy Management System

• Type: Article in International Conference Proceedings Book
• Date: 2024

Authors

Osório, GJ (Author) Other; Teixeira-Lopes, N (Author) Other; Javadi, MS (Author) Other; Catalao, JPS (Author) FEUP

Conference proceedings International

Title: 2024 INTERNATIONAL CONFERENCE ON SMART ENERGY SYSTEMS AND TECHNOLOGIES, SEST 2024

Pages: 1-6

International Conference on Smart Energy Systems and Technologies (SEST) - Driving the Advances for Future Electrification

Torino, ITALY, SEP 10-12, 2024

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Other information

Authenticus ID: P-017-B3W

DOI: 10.1109/sest61601.2024.10694099

Abstract (EN): With technological advancement and the urgency to decarbonize energy consumption habits, smart grids have gained special prominence in recent years, highlighting the importance of the massive integration of endogenous renewable sources and decision-making tools, like forecasting tools. The relevance and accuracy of the forecast make it possible to add a contribution to energy management tools in residential communities, from the point of view of end-users and the distribution network operator. This work presents the development of a short-term hybrid forecasting model, combining Long-Short Term Memory (LSTM) model forecast with the Holt-Winters forecast model, where the ability of the LSTM stands out in capturing the complex temporal patterns of historical time series, while Holt-Winters deals with trends and seasonality of historical data. Combining these models results in an intelligent hybrid system capable of efficiently dealing with the complexity inherent to renewable energy. Then, the forecasted results from load and solar generation are introduced on the home energy management model considering a small residential community, showing the relevance of accurate forecasted results tools to assist in the making decisions processes.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6