Low-cost and simple short-term load forecasting for energy management systems in small and middle-sized office buildings

Abstract

This paper deals with short-term load forecasting for energy management systems in small and middle-sized buildings. Unlike existing studies that focus on the forecasting accuracy, this study examined some candidate load forecasting methods with regard to convenience and cost-efficiency. Three-year energy use patterns of office buildings were identified according to calendar data and weather data. Simple forecasting equations were derived based on regression analyses using linear, seasonal linear, and quadratic models. The quadratic model was found most appropriate for Korea’s climate with four distinct seasons. The forecasting equation derived from the energy consumption of 2017 was verified by comparing the energy consumption forecast obtained by applying the weather data of 2018 to the equation and the actual energy consumption of 2018. This study will be using our simple load forecasting system that does not need to install sensors in all the target buildings but only in some representative buildings of similar shapes and calculate energy consumption forecasts for each target building by using the least possible data.