Accuracy of Ann Based Day-Ahead Load Forecasting in Turkish Power System: Degrading and Improving Factors

Abstract

This paper presents development of a day ahead load forecasting (DALF) model for Turkish power system with an artificial neural network (ANN). Effects of special holidays including national and religious days, and hourly random load deviations observed in Turkish power system due to significant arc furnace loads are discussed. Performance of the ANN is investigated in the sense of both DALF performance-in terms of both daily mean absolute percentage error (MAPE) and hourly absolute percentage error (APE)-and hourly secondary reserves required to ensure supply/demand adequacy of the system. The most sensitive cities to DALF in terms of daily city temperature forecasts are ranked in order to reduce the input of the developed ANN and thereby to improve execution of the model. Candidate cities are determined based on both their placement with respect to climatic zones of the country and their contribution to the system load during peak hours. The results show that, although a well-trained ANN could provide very satisfactory daily MAPEs at non-special days, such as ∼1%, the hourly absolute percentage errors (APE) could be significant due to large random load disturbances, which necessitate special attention during the day ahead allocation of hourly secondary reserves. By limiting the temperature data set with major cities, the input of ANN reduces significantly while not disturbing the MAPEs. Main contributions of the study are; addressing both benefits of the prioritizing the cities in a power system in the sense of their temperature forecast effects on the DALF performance and assessing the performance of DALF in the sense of necessary amount of secondary reserves in power systems which include significant random load deviations (e.g., large arc furnace loads).

Developing a Mixed Neural Network Approach to Forecast the Residential Electricity Consumption Based on Sensor Recorded Data

“…Based on an artificial neural network approach, in [ 25 ] the authors develop a day ahead load forecasting method in the case of the power system in Turkey, taking into account the load variation caused by national or religious holidays, in contrast to our research in which we have considered the timestamps datasets as exogenous variables that influence the residential electricity consumption. In order to evaluate the obtained performance, we have used as performance metrics the mean squared error, the error histogram, the regressions, the error autocorrelation, while in [ 25 ] the authors take into account the mean absolute percentage error and the hourly absolute percentage error.…”

“…Using an ANN approach, the authors of [ 25 ] developed a day ahead load forecasting method in the case of the power system in Turkey, taking into account the load variation caused by national or religious holidays. In order to evaluate the obtained performance, the authors take into account the mean absolute percentage error and the hourly absolute percentage error.…”

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“…In [30], the authors proposed a model for predicting the day ahead load forecasting in the case of the power system in Turkey, based on an artificial neural network approach that takes into account the load variation caused by national or religious holidays. In what concerns the performance Energies 2017, 10, 1727 3 of 36 metrics, the authors used the mean absolute percentage error and the hourly absolute percentage error.…”

“…For example, comparing with [28], in which the authors do not consider the meteorological data and with [30], in which the authors consider only national and religious holidays, we have introduced some specific exogenous variables (meteorological and timestamps datasets) that influence the hypermarket's consumption. Comparing with [29], in which the authors have used the feed-forward ANNs in order to develop a short-term forecasting of the Greek Power System load demand, our method is based on ANNs developed based on the NAR and NARX models, that are more suitable in forecasting the time series' future terms.…”