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

Dongho Lee

doi:10.1177/0144598719900964

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.

Highlights

An energy management system (EMS) facilitates efficient energy use by monitoring and controlling the energy supply and demand
This study examined load forecasting functions and their performance from the perspective of simplicity and cost efficiency for application to the EMSs of small and middle-sized buildings
The correlations between energy consumption data and weather data such as temperature, wind speed, humidity, and solar radiation were examined by using linear, seasonal linear, and quadratic models, and through multiple regression analysis

Summary

Introduction

An energy management system (EMS) facilitates efficient energy use by monitoring and controlling the energy supply and demand. A small-scale EMS was used to predict the energy consumption in a building or a plant to determine the optimal size of the renewable energy source and calculate the installed capacity of the ESS. Engineering methods predict the energy consumption based on the detailed data of the building characteristics, HVAC system, and weather conditions, which influence the energy performance. This study employs statistical methods that utilize only accessible data, rather than artificial intelligence-based methods, which require extensive data and constant CPU operation, or engineering methods, which require the information on major energy-use equipment and building characteristics. This study targets a simple and low-cost load forecasting method for office buildings in South Korea with four distinct seasons

Objective

Findings

Conclusion