Abstract
This paper gives and analyses data-driven prediction models for the energy usage of appliances. Data utilized include readings of tempera-ture and humidity sensors from a wireless network. The building envelope is meant to minimize energy demand or the energy required to power the house independent of the appliance and mechanical system efficiency. Approx-imating a mapping function between the input variables and the continuous output variable is the work of regression. The paper discusses the forecast-ing framework FOPF (Feature Optimization Prediction Framework), which includes feature selection optimization: by removing non-predictive param-eters to choose the best-selected feature hybrid optimization technique has been approached. k-nearest neighbors (KNN) Ensemble Prediction Models for the data of the energy use of appliances have been tested against some bases machine learning algorithms. The comparison study showed the powerful, best accuracy and lowest error of KNN with RMSE = 0.0078. Finally, the suggested ensemble model's performance is assessed using a one-way analysis of variance (ANOVA) test and the Wilcoxon Signed Rank Test. (Two-tailed P-value: 0.0001).