Short-term Electricity Load Forecasting Model and Bayesian Estimation for Thailand Data

Abstract

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.

Performance Analysis of Short-Term Electricity Demand with Atmospheric Variables

“…A lag structure-based model for MLR is constructed to capture seasonality because lag structures alone cannot capture the complete seasonal features during weekends [49]. The reason is explained by Clements et al [2]: the electricity demand for Saturday and Sunday was significantly over-predicted (negative bias in the errors).…”

Short‐term nodal load forecasting based on machine learning techniques

“…In Ref. 22, the Bayesian approach was also used to select parameters for an autoregressive method for modeling and forecasting a day-ahead electricity load. However, the researchers used the original dimension data to do the modeling, which has a high computation burden and may jeopardize accuracy.…”

Improvement of performance of short term electricity demand model with meteorological parameters

“… Semi/non-parametric method: emphasize non-linearity of demand [4,5] .  Multiple equation time series model: these models got high attention during latest papers followed by Peirson and Henely [6], Ramanathan et al [7], and papers [8,9,10,11,12] where each period is treated as a separate forecasting problem with its own equation.…”

“…However, their individual affects were not discussed which is our concern. Friedrich L. et al [11] investigate the results for Abu Dhabi city electricity load using multiple weather variable for 24 hour to 48 hour prediction horizon and got very promising result of 1.5 % MAPE for 24-hour and 48-hour horizon.…”

“…That means the performance is improved and consistent nature with 20 training patterns and compare the results with univariate model having no other due to such meteorological variables. Friedrich L. et al[11] investigate the results for Abu Dhabi city electricity load using multiple weather variable for 24 hour to 48 hour prediction horizon and got very promising result of 1.5 % MAPE for 24-hour and 48-hour horizon. Apadula et al[19] analyze weather, and calendar variables effects on monthly electricity demand using MLR model for Italy.…”