An Optimized Machine Learning Approach for Forecasting Thermal Energy Demand of Buildings

Abstract

Recent developments in indirect predictive methods have yielded promising solutions for energy consumption modeling. The present study proposes and evaluates a novel integrated methodology for estimating the annual thermal energy demand (DAN), which is considered as an indicator of the heating and cooling loads of buildings. A multilayer perceptron (MLP) neural network is optimally trained by symbiotic organism search (SOS), which is among the strongest metaheuristic algorithms. Three benchmark algorithms, namely, political optimizer (PO), harmony search algorithm (HSA), and backtracking search algorithm (BSA) are likewise applied and compared with the SOS. The results indicate that (i) utilizing the properties of the building within an artificial intelligence framework gives a suitable prediction for the DAN indicator, (ii) with nearly 1% error and 99% correlation, the suggested MLP-SOS is capable of accurately learning and reproducing the nonlinear DAN pattern, and (iii) this model outperforms other models such as MLP-PO, MLP-HSA and MLP-BSA. The discovered solution is finally expressed in an explicit mathematical format for practical uses in the future.

Novel Neural Network Optimized by Electrostatic Discharge Algorithm for Modification of Buildings Energy Performance

“…It is widely accepted that depending on the type of problem at hand (i.e., regression or classification problem) there are various statistical measures for evaluating the performance of machine learning models. In many similar works, utilizing a correlation index along with error criteria provided a reliable accuracy assessment for prediction tasks that use regression data [53][54][55], which is why the R P is taken into consideration associated with two well-known error criteria (i.e., the RMSE and MAE). Specifically, while the RMSE and MAE focus on the difference between the prediction and reality, R P addresses the consistency of results by reporting a value in [0, 1].…”

“…Recently, large attention has been paid to metaheuristic-optimized models, particularly newly developed algorithms. For example, the use of the symbiotic organism search algorithm for analyzing building thermal load was recommended by Rastbod, Rahimi, Dehghan, Kamranfar, Benjeddou and Nehdi [55]. This model also was found to outperform the political optimizer, harmony search algorithm, and backtracking search algorithms.…”

Identifying and Ranking the Dimensions of Urban Resilience and Its Effect on Sustainable Urban Development in Tongdejie, China

“…The concept of environmental resilience pertains to the reduction in risks associated with hazards, the return of ecological and environmental services that sustain life after crises, and the use of learning processes to reduce vulnerabilities and future risk. To achieve sustainable construction, it is essential to gain a thorough understanding of the early energy performance of buildings [54]. Alternatively, the use of renewable energy involves the use of all renewable energy sources, such as the sun, geothermal energy, wind, tides, waves, etc.…”

A Partial Least Squares Structural Equation Modelling Analysis of the Primary Barriers to Sustainable Construction in Iran

“…Renewable energy resources are seen as a viable solution for preventing global warming [2]. For instance, achieving a comprehensive understanding of a building's thermal performance is a vital step in the creation of a sustainable structure [3]. Additionally, the construction industry greatly improves the quality of human life [4].…”