Performance Analysis of Short-Term Electricity Demand with Atmospheric Variables
Kamal Chapagain () and
Somsak Kittipiyakul ()
Additional contact information
Kamal Chapagain: Sirindhorn International Institute of Technology, Thammasat University, PathumThani 12000, Thailand
Somsak Kittipiyakul: Sirindhorn International Institute of Technology, Thammasat University, PathumThani 12000, Thailand
Energies, 2018, vol. 11, issue 4, 1-34
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.
Keywords: accuracy improvement; atmospheric variables; base temperature; Bayesian estimation; short-term demand forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:11:y:2018:i:4:p:818-:d:139250
Access Statistics for this article
Energies is currently edited by Prof. Dr. Enrico Sciubba
More articles in Energies from MDPI, Open Access Journal
Bibliographic data for series maintained by XML Conversion Team ().