Journal Article

Proposing a hybrid metaheuristic optimization algorithm and machine learning model for energy use forecast in non-residential buildings

Truong, Ngoc-Son, Pham, Anh-Duc, Pham, Vu Hong Son, Huynh, Nhat-To, Pham, Tuan Minh, Ngo, Ngoc-Tri, Truong, Thi Thu Ha

2022

Overview

The building sector is the largest energy consumer accounting for 40% of global energy usage. An energy forecast model supports decision-makers to manage electric utility management. Identifying optimal values of hyperparameters of prediction models is challenging. Therefore, this study develops a novel time-series Wolf-Inspired Optimized Support Vector Regression (WIO-SVR) model to predict 48-step-ahead energy consumption in buildings. The proposed model integrates the support vector regression (SVR) and the grey wolf optimizer (GWO) in which the SVR model serves as a prediction engine while the GWO is used to optimize the hyperparameters of the SVR model. The 30-min energy data from various buildings in Vietnam were adopted to validate model performance. Buildings include one commercial building, one hospital building, three authority buildings, three university buildings, and four office buildings. The dataset is divided into the learning data and the test data. The performance of the WIO-SVR was superior to baseline models including the SVR, random forests (RF), M5P, and decision tree learner (REPTree). The WIO-SVR model obtained the highest value of correlation coefficient (R) with 0.90. The average root-mean-square error (RMSE) of the WIO-SVR was 2.02 kWh which was more accurate than those of the SVR model with 10.95 kWh, the RF model with 16.27 kWh, the M5P model with 17.73 kWh, and the REPTree model with 26.44 kWh. The proposed model improved 442.0–1207.9% of the predictive accuracy in RMSE. The reliable WIO-SVR model provides building managers with useful references in efficient energy management.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 639/166/986 / 639/166/987 / Buildings / Correlation coefficient / Electric utilities / Energy / Energy consumption / Energy usage / Humanities and Social Sciences / Learning algorithms / Machine learning / multidisciplinary / Prediction models / Residential areas / Science / Science (multidisciplinary)