Document Type
Article
Source of Publication
Energy Conversion and Management: X
Publication Date
10-1-2024
Abstract
Accurate forecasting of solar power output and load demand is critical for the efficient operation and management of isolated microgrids, where reliability and sustainability are paramount. Traditional methods often struggle with data scarcity, limitations in capturing intricate temporal dynamics, and lack of scalability. This research introduces a novel multi-task learning (MTL) model, the Self-Aware Quantized Multi-Task ConvLSTM (SAQ-MTCLSTM), which addresses these challenges by jointly forecasting solar power and load demand while leveraging shared representations across these interdependent time series. The SAQ-MTCLSTM incorporates a sophisticated architecture that combines convolutional and LSTM layers with self-aware quantization to enhance computational efficiency and model adaptability. This allows for effective knowledge transfer, improved data utilization, and the capture of intricate temporal patterns. Evaluated on a real-world isolated microgrid dataset from the Micro Reseau Mafate research project, the model demonstrates significant improvements in forecasting accuracy, with MSE values of 0.0021 for solar power output and 0.0037 for load demand forecasting, compared to single-task and other advanced models. Extensive experiments highlight the impact of data scarcity, seasonality patterns, and microgrid topology on forecasting performance. Such forecasting is essential to optimize the integration and utilization of renewable resources, enhancing operational stability and reducing dependency on external energy supplies.
DOI Link
ISSN
Publisher
Elsevier BV
Volume
24
Disciplines
Computer Sciences
Keywords
ConvLSTM, Energy efficiency, Isolated microgrids, Load demand prediction, Microgrids, Multi-task learning, Quantized neural networks, Renewable energy management, Self-aware quantization, Solar power forecasting
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Lodhi, Ehtisham; Dahmani, Nadia; Bukhari, Syed Muhammad Salman; Gyawali, Sujan; Thapa, Sanjog; Qiu, Lin; Zafar, Muhammad Hamza; and Akhtar, Naureen, "Enhancing microgrid forecasting accuracy with SAQ-MTCLSTM: A self-adjusting quantized multi-task ConvLSTM for optimized solar power and load demand predictions" (2024). All Works. 6868.
https://zuscholars.zu.ac.ae/works/6868
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series