Forecasting the electricity usage in single-family housings of a case study via kho-kho optimization algorithm

Abstract Nowadays, the consumption of energy has increased on an unprecedented scale, so it is of primary importance to forecast energy use via designing energy usage simulation systems. The challenge of energy management within buildings can have implications for energy management at a broader scale (national power grid). Furthermore, the application of various algorithms may enhance the comprehension of these dynamics. In this paper, a kho-kho-based Optimization Algorithm Numerical Moment Matching (kho-kho-NMM) method is employed for predicting the electricity usage in a set of single-family dwellings using the key features. Hence, for the residences that illustrate the systematic characteristics of the database, a group of indices including their correlated weights is obtained. The DesignBuilder software is used for developing the modeling of energy. By integrating these procedures, the buildings’ energy performance is evaluated in a case study, Toronto in Iowa. This method is able to be employed for making a small set of usual dwellings in order to demonstrate the energy performance in a far larger set of residences. A significant aspect of this research is its concentrated examination of private housing databases, positing that there are fundamental differences in energy consumption behaviors between residential and commercial structures. This can lead to identifying systematic relationships and reduces the complexity of calculations by focusing on a limited set of specialized housing types for energy behavior analysis within extensive datasets. The outcomes depict that the forecasted electricity usage within a year is 10,315 kWh being in 4% of the experimental data. What is more, the mean bias error (MBE) of the forecasted electricity usage over a month is 2.2% and the Deviation Constant with residuals (the Root Mean Square Error (DC-RMSE)) is 10.6%. Overall, the research findings can result in achieving a a methodology under virtually less calculation that can result in the outcomes’ conserved efficacy whenever the computing period and cost are decreasing.