TY - JOUR
T1 - Comparative Evaluation of Machine Learning Models for Building Power Prediction under Different Data Resolutions
AU - Nora Schneider
AU - Tobias Frank
JF - Journal of Smart Grids, Energy Analytics and Optimization
JO - J Smart Grids Energy Anal Optim
Y1 - 2023
VL - 2
IS - 1
SP - 52
N2 - Forecasting electricity usage is essential for boosting operational efficiency and advancing sustainability in buildings. The present work explores how the interval of data acquisition and the choice of modeling technique influence prediction precision for electricity demand in two contrasting building categories: an academic facility providing 15-minute data and an industrial facility providing hourly data. Multiple machine learning algorithms were tested—among them support vector machines (SVMs) with radial and sigmoid kernels, random forests (RFs), and deep neural networks (DNNs)—across various training/validation splits and feature combinations. Findings demonstrate that finer temporal resolution in data collection typically yields better model performance, reflected in lower RMSE, MAPE, and CV metrics, as well as higher R² values. Adding more lagged power consumption variables exerted a considerably stronger beneficial effect on forecast quality than incorporating weather-related variables. Notably, the SVM model equipped with the Radial kernel delivered the most consistent and superior results, proving especially effective at representing intricate nonlinear dependencies. By comparison, the DNN approach, despite acceptable performance on selected measures, exhibited notably higher MAPE values, raising concerns about potential overfitting. Collectively, these observations emphasize the need for deliberate decisions regarding data granularity, predictor variables, and algorithm selection to maximize forecasting reliability and thereby support smarter, more resource-efficient energy management in built environments.
UR - https://isespub.com/c289816846
ER -