Forecasting electricity consumption in commercial buildings using a machine learning approach

Prediction of electricity consumption is a key research area for efficient power grid operation. Accurate electricity consumption predictions of buildings can prevent power shortages in modern cities, reduce social costs caused by unnecessary energy supply, and support stable and efficient power grid operation. In this study, an electricity consumption prediction model is proposed using open-access data for the monthly and daily electricity consumption of 28 commercial buildings in Seo-gu, Gwangju, South Korea. In the case of the electricity consumption prediction of a building, information about specific parameters that affect energy consumption in target buildings is required. However, inappropriate parameter selection of the prediction model can lead to decreased prediction accuracy. Therefore, we propose a two-step approach to develop a highly accurate electricity consumption prediction model by overcoming the limitations of insufficient information. In the first step, the electricity consumption model of the building is derived by reflecting the characteristics of an individual building that constitutes a building community. In the second step, we use additional information, including the specific building’s features, as well as the energy facility types of the building. Using dynamic-time-warping-based clustering classification, we could infer the energy equipment information of the buildings. We apply the two-step method to develop a prediction model using machine learning methods. In addition, we propose an optimal prediction model by comparing the performance of a traditional time-series analysis technique and machine learning techniques. In this study, the proposed model performs >27.5% better than the existing model. Using the proposed model, it will be possible to accurately predict electricity consumption of commercial buildings, and it can be used as a major guideline for the power supply and demand of buildings and cities.