Abstract
Short-term load forecasting (STLF) is a fundamental tool for power networks’ proper functionality. As large consumers need to provide their own STLF, the residential consumers are the ones that need to be monitored and forecasted by the power network. There is a huge bibliography on all types of residential load forecast in which researchers have struggled to reach smaller forecasting errors. Regarding atypical consumption, we could see few titles before the coronavirus pandemic (COVID-19) restrictions, and afterwards all titles referred to the case of COVID-19. The purpose of this study was to identify, among the most used STLF methods—linear regression (LR), autoregressive integrated moving average (ARIMA) and artificial neural network (ANN)—the one that had the best response in atypical consumption behavior and to state the best action to be taken during atypical consumption behavior on the residential side. The original contribution of this paper regards the forecasting of loads that do not have reference historic data. As the most recent available scenario, we evaluated our forecast with respect to the database of consumption behavior altered by different COVID-19 pandemic restrictions and the cause and effect of the factors influencing residential consumption, both in urban and rural areas. To estimate and validate the results of the forecasts, multiyear hourly residential consumption databases were used. The main findings were related to the huge forecasting errors that were generated, three times higher, if the forecasting algorithm was not set up for atypical consumption. Among the forecasting algorithms deployed, the best results were generated by ANN, followed by ARIMA and LR. We concluded that the forecasting methods deployed retained their hierarchy and accuracy in forecasting error during atypical consumer behavior, similar to forecasting in normal conditions, if a trigger/alarm mechanism was in place and there was sufficient time to adapt/deploy the forecasting algorithm. All results are meant to be used as best practices during power load uncertainty and atypical consumption behavior.
Highlights
In residential short-term load forecasting (STLF), future power consumption is projected by applying a preestablished relationship between power load and its influence factors, or by dynamically assessing historical data and adapting the correlation of the influence factor—namely, time and/or weather—with the load [1]
In [3] we can see a comparison of convolutional neural network (CNN)-based model forecasting with multiple linear regression (MLR) and an unknown forecasting method used by the system operator (SOM) using a Romanian database of all consumers
We can see in [3] that CNN was the most accurate method used for the COVID-19 database, with a median mean absolute percentage error (MAPE) of 1.0007% relative to 1.0692% for MLR and 1.1552% for SOM
Summary
In residential short-term load forecasting (STLF), future power consumption is projected by applying a preestablished relationship between power load and its influence factors, or by dynamically assessing historical data and adapting the correlation of the influence factor—namely, time and/or weather—with the load [1] Defining this relationship is a two-part process: (a) identifying the correlation between power consumption and factors that influence that consumption, (b) quantifying the effect on consumption by using a suitable technique to estimate each factor. All papers addressing the issue of forecasting under atypical consumption used methods that were altered by the operator to address the changing consumption profile This limitation offered us a chance to focus on the consumer profile rather than on the historic trend, giving the forecasting methodology a flexibility in tackling unforeseen power consumption events
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