A New Training Method for Analyzable Structured Neural Network and Application of Daily Peak Load Forecasting

Abstract

This paper presents a daily peak load forecasting method using an analyzable structured neural network (ASNN) in order to explain forecasting reasons. In this paper, we propose a new training method for ASNN in order to explain forecasting reason more properly than the conventional training method. ASNN consists of two types of hidden units. One type of hidden units has connecting weights between the hidden units and only one group of related input units. Another one has connecting weights between the hidden units and all input units. The former type of hidden units allows to explain forecasting reasons. The latter type of hidden units ensures the forecasting performance. The proposed training method make the former type of hidden units train only independent relations between the input factors and output, and make the latter type of hidden units train only complicated interactions between input factors.The effectiveness of the proposed neural network is shown using actual daily peak load. ASNN trained by the proposed method can explain forecasting reasons more properly than ASNN trained by the conventional method. Moreover, the proposed neural network can forecast daily peak load more accurately than conventional neural network trained by the back propagation algorithm.

Filter‐Based Short‐Term Electric Load Prediction Considering Characteristics of Load Curve

“…10 and 11, the electric load prediction was performed well by the prediction method proposed in this paper. [6], the prediction error is 2.83% under the proposed method as compared to a prediction error of 2.53% for MEP, and although this error is 0.3% lower, it is within the prediction error of 3% for which electric power companies strive, so that the proposed method can be considered a good prediction method. However, the  ∞ filter gives more precise results for the MEB prediction on average when looking at the predictions for 30 days in Fig.…”

“…In performing parameter estimation, we used the data for the past 30 days from the prediction start date as the past data for the simulations. [6]. In this paper, we use data for the 30 days before the prediction day, taking into consideration past data and the load difference due to season.…”

“…Previously, the literature [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] has included research on short-term electric load prediction. Refs.…”

“…Refs. [6] proposes a clarification of forecasting with a hierarchical NN and improves its explanatory capability. A feature of NNs is that they can make more precise forecasts than other forecasting methods and can readily reflect the intentions of their designers.…”

“…[17]. [6] and [17]. This prediction method has the advantages of assuming a state with the worst possible noise and improving prediction accuracy by setting the gain so that the prediction error is minimized.…”