Demand Analysis of Energy Consumption in a Residential Apartment using Machine Learning

eXplainable AI (XAI)-Based Input Variable Selection Methodology for Forecasting Energy Consumption

“…Sunshine amount [46] and visibility range [47] affect energy consumption as factors that mainly determine people's indoor and outdoor activities. Energy consumption forecasting models were evaluated based on these input variables (Table 2), and these results were similar to those of previous studies, but slightly different (R 2 = 0.85, MAE = 15, RMSE = 8.83 in XGBoost [9]; R 2 = 0.32, MAE = 0.65, RMSE = 0.68 in SVR [48]; MAE = 4.16, RMSE = 5.06 in LightGBM [49]; MAPE = 28.248, RMSE = 0.127 in LSTM [32]). These differences are thought to be caused by the characteristics of the measured building or the type of energy.…”

Biomass supply chain resilience: integrating demand and availability predictions into routing decisions using machine learning

“…In a similar study, Haque, et al, compare the results of support vector regression, random forest regression, and kNN regression for building demand prediction. They conclude that multi-variant nature among the independent variables corresponding to the dependent one decreases the performance of the algorithms [43]. These studies have mostly showed how well the predictive algorithms could forecast the amounts of building demands in accordance with their energy system and building configurations, i.e., storage, etc., and what challenges and limitations each algorithm have had.…”

Forecasting automobile gasoline demand in Australia using machine learning-based regression

“…Machine learning, proposed by Samuel (1959), has been widely used in the fields of energy and economics with diverse applications, for example, the optimization of energy inputs Abdelaziz et al, 2016;Nabavi-Pelesaraei et al, 2017;Ali & Abd Elazim, 2018;Khanali et al, 2021), the investigation of energy efficiency , forecasting energy commodity prices (Ding, 2018;Yu et al, 2017;Zhang et al, 2015), forecasting energy demand (Yang et al, 2014;Panapakidis and Dagoumas, 2017;Ou et al, 2020;Haque et al, 2021). Popular ML techniques in the relevant literature include applied artificial neural networks (ANN) (Olanrewaju et al, 2013;Kunwar et al, 2013); deep learning (Lago et al, 2018;Peng et al, 2018), support vector machine (SVM) (Papadimitriou et al, 2014;Zhu et al, 2016;Jiang et al, 2018), decision trees (Bastardie et al, 2013;Zhao and Nie, 2020) and ensemble methods (Ghasemi et al, 2016;Mirakyan et al, 2017).…”