Version 1
: Received: 18 September 2024 / Approved: 19 September 2024 / Online: 20 September 2024 (02:59:58 CEST)
How to cite:
Cabezuelo, D.; Lopez-Ramirez, I.; Urkizu, J.; Goikoetxea, A. Optimizing Power Forecasting Models with Customized Features for Academic and Industrial Buildings. Preprints2024, 2024091563. https://doi.org/10.20944/preprints202409.1563.v1
Cabezuelo, D.; Lopez-Ramirez, I.; Urkizu, J.; Goikoetxea, A. Optimizing Power Forecasting Models with Customized Features for Academic and Industrial Buildings. Preprints 2024, 2024091563. https://doi.org/10.20944/preprints202409.1563.v1
Cabezuelo, D.; Lopez-Ramirez, I.; Urkizu, J.; Goikoetxea, A. Optimizing Power Forecasting Models with Customized Features for Academic and Industrial Buildings. Preprints2024, 2024091563. https://doi.org/10.20944/preprints202409.1563.v1
APA Style
Cabezuelo, D., Lopez-Ramirez, I., Urkizu, J., & Goikoetxea, A. (2024). Optimizing Power Forecasting Models with Customized Features for Academic and Industrial Buildings. Preprints. https://doi.org/10.20944/preprints202409.1563.v1
Chicago/Turabian Style
Cabezuelo, D., June Urkizu and Ander Goikoetxea. 2024 "Optimizing Power Forecasting Models with Customized Features for Academic and Industrial Buildings" Preprints. https://doi.org/10.20944/preprints202409.1563.v1
Abstract
Power consumption prediction is a crucial component in enhancing the efficiency and sustainability of building operations. This study investigates the impact of data collection frequency and model selection on the predictive accuracy of power consumption in two distinct building types: an Academic one with 15-minute interval data and an Industrial one with hourly data. Various machine learning models, including Support Vector Machine (SVM) with Radial and Sigmoid kernels, Random Forest (RF), and Deep Neural Networks (DNN), across different data splits and feature sets. Our analysis reveals that higher data collection frequency generally improves model performance, as indicated by lower RMSE, MAPE, and CV values, alongside higher R² scores. The inclusion of more historical power consumption features has also been found to have a more significant impact on the accuracy of predictions than including climate condition features. Moreover, the SVM-Radial model consistently outperformed others, particularly in capturing complex, non-linear patterns in the data. However, the DNN model, while competent in some metrics, showed elevated MAPE values, suggesting potential overfitting issues. These findings suggest that careful consideration of data frequency, features and model selection is essential for optimizing power prediction, contributing to more efficient power management strategies in building operations.
Keywords
power consumption prediction; machine learning models; predictive analytics; feature analysis; random forest; support vector machine (SVM); deep neuronal networks (DNN)
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
