Journal ArticleDOI
Deep learning with long short-term memory neural networks combining wavelet transform and principal component analysis for daily urban water demand forecasting
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TLDR
Wang et al. as discussed by the authors proposed a hybrid long short-term memory model combining with discrete wavelet transform (DWT) and principal component analysis (PCA) pre-processing techniques for water demand forecasting, i.e., DWT-PCA-LSTM.Abstract:
A reliable and accurate urban water demand forecasting plays a significant role in building intelligent water supplying system and smart city. Due to the high frequency noise and complicated relationships in water demand series, forecasting the urban water demand is not an easy task. In order to improve the model’s abilities in handling the complex patterns and catching the peaks in time series, we propose a hybrid long short-term memory model combining with discrete wavelet transform (DWT) and principal component analysis (PCA) pre-processing techniques for water demand forecasting, i.e., DWT-PCA-LSTM. First, the outliers of water demand series are identified and smoothed by 3σ criterion and weighted average method, respectively. Then, the noise component of water demand series is eliminated by DWT method and the principal components (PCs) among influencing factors of water demand are selected by PCA method. In addition, two LSTM networks are built to yield the daily urban water demand predictions using the results of DWT and PCA techniques. At last, the superiorities of the proposed model are demonstrated by comparing with the other benchmark predictive models. The water demand from 2016 to 2020 of a waterworks located in Suzhou, China is used for the experiment. The predictive performance of the experiments are evaluated by the mean absolute percentage error (MAPE), mean absolute percentage errors of peaks (pMAPE), explain variance score (EVS) and correlation coefficient (R). The results show that the DWT-PCA-LSTM model outperforms the other models and has satisfactory performance both in catching the peaks and the average prediction accuracy.read more
Citations
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A novel deep neural network architecture for real-time water demand forecasting
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The role of deep learning in urban water management: A critical review.
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A Critical Review of Short-Term Water Demand Forecasting Tools—What Method Should I Use?
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Lei Wang,Zhengchao Liu +1 more
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Proportional integral derivative booster for neural networks-based time-series prediction: Case of water demand prediction
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References
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Deep learning with long short-term memory networks for financial market predictions
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Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm
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Journal ArticleDOI
Probabilistic individual load forecasting using pinball loss guided LSTM
TL;DR: A probabilistic load forecasting method for individual consumers is proposed to handle the variability and uncertainty of future load profiles, using a deep neural network, long short-term memory (LSTM), to model both the long-term and short- term dependencies within the load profiles.