A Multi-Step Time-Series Clustering-Based Seq2Seq LSTM Learning for a Single Household Electricity Load Forecasting
Abstract
:1. Introduction
- This paper proposes deep learning-based multi-step time-series Seq2Seq LSTM framework for the electricity load forecasting.
- This paper takes on a new vision which combines the Seq2Seq LSTM and clustering to improve the efficiency of the DR program and provides multistep lookback analysis of a single household.
- Different from the aggregated residential load, in this paper, a multi-step time-series electric load clustering and forecasting for a single household is proposed, which deals load forecasting to a DR program for supply and demand control.
2. System Model
Data Preprocessing
3. Proposed Framework
3.1. Multi-Step Time-Series Electric Load Clustering
3.2. Forecast Multiload Profiles
4. Numerical Analysis
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Parameters | Value |
---|---|
Forecasting model | GRU, RNN, BiLSTM, Seq2Seq LSTM |
Training dataset | of the total input |
Testing dataset | of the total input |
Validation split | of the train dataset |
Minmax normalization | to 1 |
Regularization | dropout each layer |
Number of sequence | 64 units |
Number of lookback | 60, 120, and 180 periods |
Nnumber of maximal epochs | 100 |
Optimization algorithm | Adam |
Testing evaluation metrics | MAE, MAPE, RMSE |
Forecasting Models | MAE | MAPE | RMSE |
---|---|---|---|
LSTM 60 timesteps | 44.3 | 11.93 | 92.91 |
RNN 60 timesteps | 55.7 | 17.99 | 102.37 |
GRU 60 timesteps | 39.2 | 12.20 | 84.97 |
BiLSTM 60 timesteps | 40.3 | 12.32 | 88.41 |
Seq2Seq LSTM 60 timesteps (proposed) | 35.1 | 10.93 | 82.75 |
LSTM 120 timesteps | 50.9 | 16.22 | 98.35 |
RNN 120 timesteps | 61.2 | 20.10 | 109.28 |
GRU 120 timesteps | 36.6 | 10.87 | 82.71 |
BiLSTM 120 timesteps | 47.2 | 13.23 | 91.63 |
Seq2Seq LSTM 120 timesteps (proposed) | 46.5 | 12.22 | 86.50 |
LSTM 180 timesteps | 48.9 | 13.09 | 99.50 |
RNN 180 timesteps | 67.3 | 22.47 | 113.92 |
GRU 180 timesteps | 42.9 | 13.25 | 88.60 |
BiLSTM 180 timesteps | 41.6 | 11.19 | 89.75 |
Seq2Seq LSTM 180 timesteps (proposed) | 38.5 | 13.32 | 88.65 |
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Masood, Z.; Gantassi, R.; Ardiansyah; Choi, Y. A Multi-Step Time-Series Clustering-Based Seq2Seq LSTM Learning for a Single Household Electricity Load Forecasting. Energies 2022, 15, 2623. https://doi.org/10.3390/en15072623
Masood Z, Gantassi R, Ardiansyah, Choi Y. A Multi-Step Time-Series Clustering-Based Seq2Seq LSTM Learning for a Single Household Electricity Load Forecasting. Energies. 2022; 15(7):2623. https://doi.org/10.3390/en15072623
Chicago/Turabian StyleMasood, Zaki, Rahma Gantassi, Ardiansyah, and Yonghoon Choi. 2022. "A Multi-Step Time-Series Clustering-Based Seq2Seq LSTM Learning for a Single Household Electricity Load Forecasting" Energies 15, no. 7: 2623. https://doi.org/10.3390/en15072623