Regional Residential Short-Term Load-Interval Forecasting Based on SSA-LSTM and Load Consumption Consistency Analysis

Ruixiang Zhang, Ziyu Zhu, Meng Yuan, Yihan Guo, Jie Song, Xuanxuan Shi, Yu Wang () and Yaojie Sun ()
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Ruixiang Zhang: School of Information Science and Technology, Fudan University, Shanghai 200433, China
Ziyu Zhu: School of Information Science and Technology, Fudan University, Shanghai 200433, China
Meng Yuan: School of Information Science and Technology, Fudan University, Shanghai 200433, China
Yihan Guo: School of Information Science and Technology, Fudan University, Shanghai 200433, China
Jie Song: Nari Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, China
Xuanxuan Shi: State Grid Nanjing Power Supply Company, Nanjing 210019, China
Yu Wang: School of Information Science and Technology, Fudan University, Shanghai 200433, China
Yaojie Sun: School of Information Science and Technology, Fudan University, Shanghai 200433, China

Energies, 2023, vol. 16, issue 24, 1-17

Abstract: The electricity consumption behavior of the inhabitants is a major contributor to the uncertainty of the residential load system. Human-caused uncertainty may have a distributional component, but it is not well understood, which limits further understanding the stochastic component of load forecasting. This study proposes a short-term load-interval forecasting method considering the stochastic features caused by users’ electricity consumption behavior. The proposed method is composed of two parts: load-point forecasting using singular spectrum analysis and long short-term memory (SSA-LSTM), and load boundaries forecasting using statistical analysis. Firstly, the load sequence is decomposed and recombined using SSA to obtain regular and stochastic subsequences. Then, the load-point forecasting LSTM network model is trained from the regular subsequence. Subsequently, the load boundaries related to load consumption consistency are forecasted by statistical analysis. Finally, the forecasting results are combined to obtain the load-interval forecasting result. The case study reveals that compared with other common methods, the proposed method can forecast the load interval more accurately and stably based on the load time series. By using the proposed method, the evaluation index coverage rates (CRs) are (17.50%, 1.95%, 1.05%, 0.97%, 7.80%, 4.55%, 9.52%, 1.11%), (17.95%, 3.02%, 1.49%, 5.49%, 5.03%, 1.66%, 1.49%), (19.79%, 2.79%, 1.43%, 1.18%, 3.37%, 1.42%) higher than the compared methods, and the interval average convergences (IACs) are (−18.19%, −8.15%, 3.97%), (36.97%, 21.92%, 22.59%), (12.31%, 21.59%, 7.22%) compared to the existing methods in three different counties, respectively, which shows that the proposed method has better overall performance and applicability through our discussion.

Keywords: load-interval forecasting; long short-term memory; regional residential load; uncertainty analysis; singular spectrum analysis; load consumption consistency (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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