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

Abstract

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.

Building projects with time–cost–quality–environment trade-off optimization using adaptive selection slime mold algorithm

“…To prove the researchability of the article, the author has also made an in-depth study on algorithms for solving multiobjectives. Specifically, Ngoc-Tri Ngo et al ( 2022 ) presented the meta-model optimization algorithm combined with computer models to solve the energy use of buildings. Son and Lien ( 2022 ) introduced a case study of crowd-sourced arbitration using the Rhubarb platform to resolve disputes.…”

Optimization in Construction Management Using Adaptive Opposition Slime Mould Algorithm

“…In the area of construction management, the author has also conducted a considerable study on a wide range of problems connected to artifcial intelligence. A metamodel optimization approach in conjunction with computer models was specially provided to address the energy consumption of buildings [25]. To lower the cost of establishing water distribution networks, the GWO-HHO is being researched [26].…”

A Comprehensive Predictive-Learning Framework for Optimal Scheduling and Control of Smart Home Appliances Based on User and Appliance Classification

“…We have shown the energy consumption results for super-active user using different optimization models. The existing works that we have used for comparison are WIO-SVR [ 38 ], EM_WOA [ 39 ], PSO [ 40 ], and GWO [ 41 ].…”