Abstract
In order to reduce the effect of numerical weather prediction (NWP) error on short term load forecasting (STLF) and improve the forecasting accuracy, a new hybrid model based on support vector regression (SVR) optimized by an artificial bee colony (ABC) algorithm (ABC-SVR) and seasonal autoregressive integrated moving average (SARIMA) model is proposed. According to the different day types and effect of the NWP error on forecasting prediction, working days and weekends load forecasting models are selected and constructed, respectively. The ABC-SVR method is used to forecast weekends load with large fluctuation, in which the best parameters of SVR are determined by the ABC algorithm. The working days load forecasting model is constructed based on SARIMA modified by ABC-SVR (AS-SARIMA). In the AS-SARIMA model, the ability of SARIMA to respond to exogenous variables is improved and the effect of NWP error on prediction accuracy is reduced more than with ABC-SVR. Contrast experiments are constructed based on International Organization for Standardization (ISO) New England load data. The experimental results show that prediction accuracy of the proposed method is less affected by NWP error and has higher forecasting accuracy than contrasting approaches.
Highlights
With the continuous development of smart grids, short term load forecasting (STLF) results have become the important basis for dynamic pricing in the power market
In order to compare the prediction accuracy of optimal approaches and analyze the forecasting performance affected by numerical weather prediction (NWP) errors of the two models, artificial bee colony (ABC)-support vector regression (SVR) and AS-seasonal autoregressive integrated moving average (SARIMA) are used performance affected by NWP errors of the two models, ABC-SVR and AS-SARIMA are used to to forecast the load from 20 to 26 February in the scenarios of actual temperature and noisy forecast the load from 20 to 26 February in the scenarios of actual temperature and noisy temperature
The forecast curves of ABC-SVR and AS-SARIMA are shown in Figure 7, and the mean absolute percentage error (MAPE) of the two of the two models are listed in Table 3
Summary
With the continuous development of smart grids, short term load forecasting (STLF) results have become the important basis for dynamic pricing in the power market. Electricity prices formulated based on results of STLF lead electricity consumption during the off-peak period, reduce differences between peak and valley loads and ensure the economic operation of the power system. Feature sets are the foundation of constructing STLF models. In addition to historical loads, other exogenous variables such as temperature and day types which affect the accuracy of STLF should be considered. The temperature data used to construct the feature sets is obtained from numerical weather prediction (NWP), where prediction errors exist, so the influence of NWP errors on load forecasting precision should be considered when establishing any forecast model [5,6]
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