Forecasting Power Demand in Complex Buildings Using Machine Learning: A Shopping Center Case Study

• Type: Article in International Conference Proceedings Book
• Date: 2025-07

Authors

Bruno Palley (Author) FEUP; Hermano Bernardo (Author) Other; João Poças Martins (Author) FEUP; Rosaldo J. F. Rossetti (Author) FEUP

Conference proceedings International

Title: Technological Innovation for AI-Powered Cyber-Physical Systems - 16th IFIP WG 5.5 / SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2025, Caparica, Portugal, July 2-4, 2025, Proceedings

Pages: 101-112

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-019-4J3

DOI: 10.1007/978-3-031-97051-1_7

Abstract (EN): Recent studies have focused on forecasting power demand in buildings to enhance energy management. However, the literature still lacks comparative analyses of power demand forecasting algorithms. In addition, more case studies involving different building typologies are needed, as each building exhibits distinct behavior and load profiles. This paper aims to develop machine learning models to forecast the power demand of a large shopping center in the northern region of Portugal. The main objective is to compare the performance of several machine learning models. The results are promising, demonstrating adequate performance even during most holidays.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11