Affiliation:
1. The State Key Laboratory of ASIC and System Fudan University Shanghai China
2. Department of Automation Shanghai Jiao Tong University Shanghai China
3. Department of Economics, Department of Statistic and Actuarial Science, Big Data Research Lab University of Waterloo Waterloo Canada
4. Senior Research Fellow Harvard University Cambridge Massachusetts USA
5. The Key Laboratory of Control of Power Transmission and Conversion of Ministry of Education Department of Electrical Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University, Minhang District Shanghai China
Abstract
AbstractNumerous studies on short‐term load forecasting (STLF) have used feature extraction methods to increase the model's accuracy by incorporating multidimensional features containing time, weather and distance information. However, less attention has been paid to the input data size and output dimensions in STLF. To address these two issues, an STLF model is proposed based on output dimensions using only load data. First, the load data's long‐term behavior (trend and seasonality) is extracted through the long short‐term memory network (LSTM), followed by convolution to obtain the load data's non‐stationarity. Then, using the self‐attention mechanism (SAM), the crucial input load information is emphasized in the forecasting process. The calculation example shows that the proposed algorithm outperforms LSTM, LSTM‐based SAM, and CNN‐GRU‐based SAM by more than 10% in eight different buildings, demonstrating its suitability for forecasting with only load data. Additionally, compared to earlier research utilizing two well‐known public data sets, the MAPE is optimized by 2.2% and 5%, respectively. Also, the method has good prediction accuracy for a wide variety of time granularities and load aggregation levels, so it can be applied to various load forecasting scenarios and has good reference significance for load forecasting instrumentation.
Funder
National Natural Science Foundation of China
Publisher
Institution of Engineering and Technology (IET)
Subject
Electrical and Electronic Engineering,Energy Engineering and Power Technology,Control and Systems Engineering
Cited by
17 articles.
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