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

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-t...

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Bibliographic Details
Published in:Energies (Basel) 2022-04, Vol.15 (7), p.2623
Main Authors: Masood, Zaki, Gantassi, Rahma, Ardiansyah, Choi, Yonghoon
Format: Article
Language:English
Subjects:
Algorithms
Appliances
Artificial intelligence
Big Data
Clustering
Datasets
Deep learning
Electrical loads
Electricity
Energy
Energy consumption
Energy management
energy management system
Forecasting
Forecasting techniques
Households
Load
load forecasting
Long short-term memory
LSTM
Neural networks
Prediction models
smart grid
Supply & demand
Sustainability
Time series
time-series prediction
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en15072623