A Multi-Step Time-Series Clustering-Based Seq2Seq LSTM Learning for a Single Household Electricity Load Forecasting

Zaki Masood, Rahma Gantassi, Ardiansyah and Yonghoon Choi
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Zaki Masood: Department of Electrical Engineering, Chonnam National University, Gwangju 61186, Korea
Rahma Gantassi: Department of Electrical Engineering, Chonnam National University, Gwangju 61186, Korea
Ardiansyah: Department of Electrical Engineering, Chonnam National University, Gwangju 61186, Korea
Yonghoon Choi: Department of Electrical Engineering, Chonnam National University, Gwangju 61186, Korea

Energies, 2022, vol. 15, issue 7, 1-11

Abstract: The deep learning (DL) approaches in smart grid (SG) describes the possibility of shifting the energy industry into a modern era of reliable and sustainable energy networks. This paper proposes a time-series clustering framework with multi-step time-series sequence to sequence (Seq2Seq) long short-term memory (LSTM) load forecasting strategy for households. Specifically, we investigate a clustering-based Seq2Seq LSTM electricity load forecasting model to undertake an energy load forecasting problem, where information input to the model contains individual appliances and aggregate energy as historical data of households. The original dataset is preprocessed, and forwarded to a multi-step time-series learning model which reduces the training time and guarantees convergence for energy forecasting. Furthermore, simulation results show the accuracy performance of the proposed model by validation and testing cluster data, which shows a promising potential of the proposed predictive model.

Keywords: deep learning; energy management system; LSTM; load forecasting; smart grid; time-series prediction (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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