Enhancing microgrid forecasting accuracy with SAQ-MTCLSTM: A self-adjusting quantized multi-task ConvLSTM for optimized solar power and load demand predictions

Enhancing microgrid forecasting accuracy with SAQ-MTCLSTM: A self-adjusting quantized multi-task ConvLSTM for optimized solar power and load demand predictions

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, a...

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Bibliographic Details
Main Authors: Ehtisham Lodhi, Nadia Dahmani, Syed Muhammad Salman Bukhari, Sujan Gyawali, Sanjog Thapa, Lin Qiu, Muhammad Hamza Zafar, Naureen Akhtar
Format: Article
Language:English
Published: Elsevier 2024-10-01
Series:Energy Conversion and Management: X
Subjects:
Microgrids
Solar power forecasting
Load demand prediction
Multi-task learning
ConvLSTM
Quantized neural networks
Online Access:http://www.sciencedirect.com/science/article/pii/S2590174524002459

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http://www.sciencedirect.com/science/article/pii/S2590174524002459

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