Performance Analysis of Short-Term Electricity Demand with Atmospheric Variables

Kamal Chapagain,Somsak Kittipiyakul

doi:10.3390/en11040818

Abstract

The quality of short-term electricity demand forecasting is essential for the energy market players for operation and trading activities. Electricity demand is significantly affected by non-linear factors, such as climatic conditions, calendar components and seasonal behavior, which have been widely reported in the literature. This paper considers parsimonious forecasting models to explain the importance of atmospheric variables for hourly electricity demand forecasting. Many researchers include temperature as a major weather component. If temperature is included in a model, other weather components, such as relative humidity and wind speed, are considered as less effective. However, several papers mention that there is a significant impact of atmospheric variables on electricity demand. Therefore, the main purpose of this study is to investigate the impact of the following atmospheric variables: rainfall, relative humidity, wind speed, solar radiation, and cloud cover to improve the forecasting accuracy. We construct three different multiple linear models (Model A, Model B, and Model C) including the auto-regressive moving average with exogenous variables (ARMAX) with the mentioned exogenous weather variables to compare the performances for Hokkaido Prefecture, Japan. The Bayesian approach is applied to estimate the weight of each variable with Gibbs sampling to approximate the estimation of the coefficients. The overall mean absolute percentage error (MAPE) performances of Model A, Model B, and Model C are estimated as 2.43%, 1.98% and 1.72%, respectively. This means that the accuracy is improved by 13.4% by including rainfall, snowfall, solar radiation, wind speed, relative humidity, and cloud cover data. The results of the statistical test indicate that these atmospheric variables and the improvement in accuracy are statistically significant in most of the hours. More specifically, they are significant during highly fluctuating and peak hours.

Highlights

Short-term electricity demand or load forecasting is a way of estimating future demand for a short time horizon, commonly an hour to one week ahead
Since our motive is to find the atmospheric impact on electricity demand, their impacts vary according to the hour of the day
Three multiple linear regression (MLR) models named as Model A, Model B, and Model C are constructed based on the types of variables used

Summary

Introduction

Short-term electricity demand or load forecasting is a way of estimating future demand for a short time horizon, commonly an hour to one week ahead. According to the time horizon for prediction, Apadula et al [1] classified the load forecast into four categories: very short-term forecasts (from a few minutes to 1 h ahead), short term forecasts (from 1 h–1 week ahead), medium-term forecasts (from one week to a year head) and long-term forecasts (longer than a year ahead) Such differences in the lead time influence the choice of models and methods to apply, as well as the selection of important external factors affecting the electricity demand (socio-economic, atmospheric, seasonal and time-dependent factors) [1]. Knowledge of electricity demand behavior in advance is crucial for the planning, analysis and operation of power systems to assure an uninterrupted, reliable, secure and economic supply of electricity. Improving the accuracy of electricity forecast could reduce the power marketing risk and keep the market stable for electricity demand and price, as well [3,4] accurate electricity load demand forecasting for different time horizons is a very hot research issue

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