A residual load modeling approach for household short-term load forecasting application

Abstract The household residual component of total power consumption can be considered as a portion of load demand describing the non-temperature-related factors. This component can be decomposed to irregular and predictable energy demands. The predictable component of the residual load include consumptions which are likely to have a periodic behavior. The modeling of this periodic part of the residual load can help to enhance the overall forecasting accuracy. This paper intends to model the periodic part of the residual component by capturing the behavioral patterns of overestimated and underestimated residuals as the divisions of the main residual component. Accordingly, in order to achieve its ambition, this work proposes an Adaptive Circular Conditional Expectation (ACCE) method on the basis of circular analysis to define the sub-residuals operation schedules. Consequently, an adaptive Linear Model (LM) procedure is employed to predict the residual component demand using the results of the ACCE process at each time window. Subsequently, the predicted residual is utilized to adaptively improve the performance of total electricity demand forecasting. The accuracy of the forecasting results is evaluated using Normalized Mean Absolute Error (NMAE). As a result, the proposed approach of the periodic residual demand modeling in a daily horizon leads to a promising accuracy increase of 23%. Furthermore, the proposed residual modeling method, in combination with the temperature-related component forecasting, can increase the total power consumption prediction performance by 7%. The efficacy of the proposed approach is examined via numerical analysis of real data.