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Performance Analysis of Short-Term Electricity Demand with Atmospheric Variables

Sirindhorn International Institute of Technology, Thammasat University, PathumThani 12000, Thailand
Author to whom correspondence should be addressed.
Current address: 131 Moo 5, Bangkadi Muang, PathumThani 12000, Thailand.
These authors contributed equally to this work.
Energies 2018, 11(4), 818;
Received: 6 March 2018 / Revised: 26 March 2018 / Accepted: 29 March 2018 / Published: 2 April 2018
PDF [2696 KB, uploaded 3 May 2018]


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. View Full-Text
Keywords: accuracy improvement; atmospheric variables; base temperature; Bayesian estimation; short-term demand forecasting accuracy improvement; atmospheric variables; base temperature; Bayesian estimation; short-term demand forecasting

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Chapagain, K.; Kittipiyakul, S. Performance Analysis of Short-Term Electricity Demand with Atmospheric Variables. Energies 2018, 11, 818.

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