BiLSTM energy consumption prediction model for buildings optimized by sparrow search algorithm
Accurate prediction of building energy consumption is crucial for the rational planning of building energy resources.To select important influencing factors and improve the prediction accuracy of the building energy consumption model during the prediction process,the paper proposes a feature selection method combining entropy-weighted K-means and random forest,and a prediction model optimized by the Sparrow Search Algorithm(SSA)for the Bidirectional Long Short-Term Memory Network(BiLSTM)for building energy consumption prediction.The results show that after feature selection,the computational accuracy of the prediction model is significantly improved.Compared with a single BiLSTM prediction model,the SSA-BiLSTM prediction model demonstrates good predictive performance in energy consumption prediction across different seasons.
energy consumption predictiondeep learningBidirectional Long Short-Term Memory Networkfeature selectionsparrow search algorithm
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