DSPM: Dual sequence prediction model for efficient energy management in micro-grid

Zulfiqar Ahmad Khan, Shabbir Ahmad Khan, Tanveer Hussain and Sung Wook Baik

Applied Energy, 2024, vol. 356, issue C, No S0306261923017038

Abstract: Power generation and consumption predictions are fundamental for smart grid operations, addressing challenges posed by renewable energy variability and irregular consumer demand. This study introduces the Dual Sequence Predictive Model (DSPM) based on Spatiotemporal CNN (STCNN) architecture, offering a holistic solution for forecasting Electricity Generation (EG) and Electricity Consumption (EC) collectively. Leveraging STCNN, the DSPM efficiently extracts spatial and temporal information, improving prediction accuracy and processing speed. A 1D spatial attention module is added to capture crucial spatial dependencies in historical data. Incorporating shared historical weather information streamlines learning process and reduces model complexity. Extensive benchmark evaluations demonstrate the DSPM superior performance, achieving the lowest error rates compared to baseline models. The DSPM also employs the Kullback–Leibler Divergence (KLD) algorithm for power generation and consumption matching, ensuring efficient power distribution within smart and microgrids. Furthermore, the DSPM is evaluated alongside Single Sequence Prediction (SSP) models, providing a comprehensive analysis of its capabilities and comparisons with state-of-the-art models. The SSP model achieved an average RMSE reduction of 17.3% for solar EG data and 15.86% for IHEPC residential EC data, while DSPM reduced RMSE by 7.57% over PowerGrid dataset compared to baseline models.

Keywords: Dual sequence prediction model; Power generation prediction; Power consumption prediction; Temporal convolutional neural network; Deep learning; Power matching; Smart/micro grid; Short-term prediction (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.apenergy.2023.122339

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