Accurate electricity demand forecasting for a short horizon is very important for day-to-day control, scheduling, operation, planning, and stability of the power system. The main factors that affect the forecasting accuracy are deterministic variables and weather variables such as types of days and temperature. Due to the tropical climate of Thailand, the marginal impact of weather variables on electricity demand is worth analyzing. Therefore, this paper primarily focuses on the impact of temperature and other deterministic variables on Thai electricity demand. Accuracy improvement is also considered during model design. Based on the characteristics of demand, the overall dataset is divided into four different subgroups and models are developed for each subgroup. The regression models are estimated using Ordinary Least Square (OLS) methods for uncorrelated errors, and General Least Square (GLS) methods for correlated errors, respectively. While Feed Forward Artificial Neural Network (FF-ANN) as a simple Deep Neural Network (DNN) is estimated to compare the accuracy with regression methods, several experiments conducted for determination of training length, selection of variables, and the number of neurons show some major findings. The first finding is that regression methods can have better forecasting accuracy than FF-ANN for Thailand’s dataset. Unlike much existing literature, the temperature effect on Thai electricity demand is very interesting because of their linear relationship. The marginal impacts of temperature on electricity demand are also maximal at night hours. The maximum impact of temperature during night hours happens at 11 p.m., is 300 MW/ ° C, about 4 % rise in demand while during day hours, the temperature impact is only 10 MW/ ° C to 200 MW/ ° C about 1.4 % to 2.6 % rise.
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.
Abstract. This paper proposes multi-equation linear regression model with autoregressive AR(2) method for modelling and forecasting a day ahead electricity load. AR(2) is used to show the dependency of next data on its previous two days data because the nature of electricity load consumption for the next day follow the pattern of previous days. Since, we allocate one equation for particular half hour, we need 48 separate equations to predict for one complete day. Parameters of model are estimated based on two different approaches-(i) Classical approach, and (ii) Bayesian approach. Classical or Ordinary Least Square approach estimates the parameters in terms of single value and hence its forecast is also single value, where as Bayesian approach includes the predictive distribution of electricity load due to multiple values of parameters. So, we can forecast electricity load not only from mean, but median and with other percentile values. In this paper, we use 70-percentile value for forecasting because the performance for all models accounts better in this percentile than that of mean and median forecast. Finally, we compare the performances where Bayesian estimation provides better and consistent performance than that of OLS estimation.
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