Abstract
Electrical load forecasting plays a crucial role in the proper scheduling and operation of power systems. To ensure the stability of the electrical network, it is necessary to balance energy generation and demand. Hence, different very short-term load forecast technologies are being designed to improve the efficiency of current control strategies. This paper proposes a new forecaster based on artificial intelligence, specifically on a recurrent neural network topology, trained with a Levenberg–Marquardt learning algorithm. Moreover, a sensitivity analysis was performed for determining the optimal input vector, structure and the optimal database length. In this case, the developed tool provides information about the energy demand for the next 15 min. The accuracy of the forecaster was validated by analysing the typical error metrics of sample days from the training and validation databases. The deviation between actual and predicted demand was lower than 0.5% in 97% of the days analysed during the validation phase. Moreover, while the root mean square error was 0.07 MW, the mean absolute error was 0.05 MW. The results suggest that the forecaster’s accuracy is considered sufficient for installation in smart grids or other power systems and for predicting future energy demand at the chosen sites.
Highlights
Predicting energy demand has a significant effect on the operation and schedule of any power system, no matter its dimensions
With regard to the Rootmean mean squared squared error error (RMSE) parameter, it must be taken where Yithat is the measured in a moment; Yi′ is the predicted value in a moment, N are the into account the error is value squared, which means that a few inaccurate forecastsand strongly distort number of predictions that are analysed
The mean absolute error (MAE) error metric is better in this study than in [10], whereas in the Mean absolute absolute percentage percentage error error (MAPE) error metric [10] obtains better results than those provided in this study
Summary
Predicting energy demand has a significant effect on the operation and schedule of any power system, no matter its dimensions. Whether the system is a microgrid or a traditional power network, forecasting load electricity demand is required if a reliable and efficient power system is desired Many elements, such as energy price, unit commitment, load dispatch for generators and so on, depend on the value obtained through load forecasters [1]. Very Short-Term Forecast (VSTF): The forecaster makes predictions for a few minutes ahead, and prognosticated values are given to the control unit for real-time dispatch This forecasting is used for getting a quick response to the intra-day energy demand fluctuations [9,10]. Due to the need to have a high standard of accuracy in an energy demand forecaster that will be implemented in a microgrid, the forecaster can be used in bigger power electric systems owing to the lower volatility of such systems, providing information to the power system operator. A sensitivity analysis was performed, examining different forecasters by changing these parameters to obtain the optimal ones
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