Ribeiro, Bruno (Polytechnic of Porto), Silva, Rafael (Polytechnic of Porto), Gomes, Luis (Polytechnic of Porto), Vale, Zita (Polytechnic Institute of Porto)

Detailed analysis of a deep learning energy forecast model considering different input units and dimensions

Scheduled for presentation during the Invited Session "Optimal operation and control in smart grids" (ThuS2T2), Thursday, July 11, 2024, 11:30−11:50, Grand Amphitheater

12th IFAC Symposium on Control of Power & Energy Systems, July 10-12, 2024, Rabat, Morocco

This information is tentative and subject to change. Compiled on January 2, 2025

Abstract

The use of smart buildings technologies in residential houses, commonly referred to as smart homes, can benefit the operation and management of the buildings as well and its users. The application of energy related models in smart buildings can improve building sustainability and efficiency, but, usually, requires the existence of energy forecasting models capable of providing accurate and precise information. In this paper, it was studied how different input configurations of an energy forecasting model impact its performance by considering multiple error metrics. The conducted study allowed a detailed analysis of each input configuration to see which one is better suitable to be integrated with the smart grid management platform developed by the authors, which is also presented in this paper. The results demonstrate that the inputs’ units and dimensions have impact on the forecast error, indicating that accumulative Wh values are potentially better for the energy forecast model considered.