Research on short-term load forecasting based on accumulated temperature effect and improved temporal convolutional network - Haihong Bian; Qian Wang; Guozheng Xu; Xiu Zhao (Energy Reports, 2022)
Aiming at the uncertainty in short-term load forecasting, and considering the multi-dimensionality of load data and the continuity of time series, this paper constructs a short-term power load forecasting method based on accumulated temperature effect and improved Temporal Convolutional Network. First, the common load influencing factors in the short-term forecasting process were explored, the data were cleaned and correlated, and a quantitative model of temperature variables that considered the effect of accumulated temperature was established, such as the weighted average method and the discrete correction model. Secondly, fully consider and divide the multi-dimensional influencing factors, the time series characteristic quantity is formed by taking advantage of the Temporal Convolutional Network (TCN), extracting potential relationships between time-series data and non-time-series data, and the optimal load estimation value is obtained. Finally, the output of the TCN is fused with the non-time-series data to form a new input feature matrix, and the load prediction is completed through the Back Propagation (BP) Neural Network. The calculation example shows that the TCN-BP model combined with temperature correction can reduce the average absolute error by 1.77%. Under the same forecasting method, the load forecasting accuracy obtained by the weighted average method is higher than that of the discrete correction model. Compared with the single TCN and BP models, the method proposed in this paper increases the training time, but reduces the time loss of prediction and improves the prediction accuracy.