What question did this study set out to answer?

To develop an accurate short-term electricity load forecasting model for individual residential customers to address high demand volatility and grid uncertainty.

September 18, 2017IEEE Transactions on Smart Grid

Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network

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Authors

WKWeicong KongSouthern University of Science and Technology ZDZhao Yang DongHebei University of Engineering YJYouwei JiaSouthern University of Science and Technology

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Implication

Computational modeling demonstrates superior short-term electricity demand prediction in individual households, indicating improved grid planning for renewable energy integration.

Key Points

To develop an accurate short-term electricity load forecasting model for individual residential customers to address high demand volatility and grid uncertainty.
Constructed a deep learning forecasting framework utilizing long short-term memory (LSTM) recurrent neural networks.
Evaluated performance using publicly available real residential smart meter data against multiple state-of-the-art benchmark algorithms.
The proposed LSTM-based framework outperformed all benchmark algorithms in forecasting accuracy for individual residential short-term electric loads.

Cite This Study

Kong et al. (2017) studied this question.

synapsesocial.com/papers/69dc2e414f901957bec0fa72 https://doi.org/10.1109/tsg.2017.2753802

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Implication

Computational modeling demonstrates superior short-term electricity demand prediction in individual households, indicating improved grid planning for renewable energy integration.

Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network

Discussion

Member takes

Implication

Key Points

Cite This Study

Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network

Discussion

Member takes

Implication

Key Points

Cite This Study

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