The diagram of the LSTM neural network.

<div><p>Accurate energy demand forecasting is critical for efficient energy management and planning. Recent advancements in computing power and the availability of large datasets have fueled the development of machine learning models. However, selecting the most appropriate features to e...

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Bibliographic Details
Main Author:	Mohd Sakib (20565228) (author)
Other Authors:	Tamanna Siddiqui (17038816) (author), Suhel Mustajab (19106497) (author), Reemiah Muneer Alotaibi (19226201) (author), Nouf Mohammad Alshareef (20565231) (author), Mohammad Zunnun Khan (20565234) (author)
Published:	2025
Subjects:	Evolutionary Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified optimize feature selection mean absolute error gated recurrent unit enhance prediction accuracy enhance model evaluation conducted ten simulations energy consumption patterns efficient energy management stacking ensemble technique lstm ), bi multiple forecasting models machine learning models genetic algorithm identifies directional long short long short genetic algorithm energy analysts ensemble approach bilstm ), weekend subsets weather variables term memory temporal characteristics study proposes robustness remains recent advancements optimal subset large datasets key challenge gru ). final forecast detailed analysis computing power combined using 42 %, 38 %,
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The diagram of the LSTM neural network.

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