EL BAKALI, Saida (ERERA, ENSAM, Mohammed V University, Rabat, Morocco), Ouadi, Hamid (Ismra), Giri, Fouad (University of Caen Normandie), GHEOUANY, Saad (ERERA, National School of Arts and Crafts, Mohammed V University), Mounir, Nada (ERERA, ENSAM, Mohammed V University, Rabat, Morocco), Jrhilifa, ismael (ERERA, National High School of Arts and Crafts, Mohammed V Unive)

Forecasting Occupants’ Presence and Photovoltaic Power Using an Enhanced Stacking Algorithm for Demand/Supply Energy Management

Scheduled for presentation during the Regular Session "Photovoltaic and Concentrated solar systems" (ThuS2T1), Thursday, July 11, 2024, 12:30−12:50, Salle des conférences

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

This paper proposes an improved stacking algorithm based on an ANN (S-ANN) model to predict occupants’ presence probability and solar photovoltaic (PV) power generation over a 24-hour horizon for demand and supply energy management. The proposed S-ANN improves on traditional stacking by training each base model on various datasets instead of the same dataset. Python is employed as the simulation software for deploying the S-ANN model and conducting the analyses. The model’s performance is evaluated using the Normalized Root Mean Square Error (NRMSE) and Normalized Mean Absolute Error (NMAE) metrics . The results establish the effectiveness of the proposed model with an NRMSE of 2.91% and NMAE of 2.20% on a cloudy day (prediction of occupant presence in the kitchen reported as an example).