Short-Term Residential Load Forecasting Based on $K-\text{shape}$ Clustering and Domain Adversarial Transfer Network

Abstract

In recent years, the expansion of the power grid has led to a continuous increase in the number of consumers within the distribution network. However, due to the scarcity of historical data for these new consumers, it has become a complex challenge to accurately forecast their electricity demands through traditional forecasting methods. This paper proposes an innovative short-term residential load forecasting method that harnesses advanced clustering, deep learning, and transfer learning technologies to address this issue. To begin, this paper leverages the domain adversarial transfer network. It employs limited data as target domain data and more abundant data as source domain data, thus enabling the utilization of source domain insights for the forecasting task of the target domain. Moreover, a $\boldsymbol{K}-\mathbf{shape}$ clustering method is proposed, which effectively identifies source domain data that align optimally with the target domain, and enhances the forecasting accuracy. Subsequently, a composite architecture is devised, amalgamating attention mechanism, long short-term memory network, and seq2seq network. This composite structure is integrated into the domain adversarial transfer network, bolstering the performance of feature extractor and refining the forecasting capabilities. An illustrative analysis is conducted using the residential load dataset of the Independent System Operator to validate the proposed method empirically. In the case study, the relative mean square error of the proposed method is within 30 MW, and the mean absolute percentage error is within 2%. A significant improvement in accuracy, compared with other comparative experimental results, underscores the reliability of the proposed method. The findings unequivocally demonstrate that the proposed method advocated in this paper yields superior forecasting results compared with prevailing mainstream forecasting methods.