A Real-Time Data Analysis Platform for Short-Term Water Consumption Forecasting with Machine Learning
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- Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
- Szoplik, Jolanta, 2015. "Forecasting of natural gas consumption with artificial neural networks," Energy, Elsevier, vol. 85(C), pages 208-220.
- Fotios Petropoulos & Evangelos Spiliotis, 2021. "The Wisdom of the Data: Getting the Most Out of Univariate Time Series Forecasting," Forecasting, MDPI, vol. 3(3), pages 1-20, June.
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- Sonia Leva, 2022. "Editorial for Special Issue: “Feature Papers of Forecasting 2021”," Forecasting, MDPI, vol. 4(1), pages 1-3, March.
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Keywords
load curve; unevenly spaced time series; long short-term memory (LSTM); back-propagation neural network (BPNN); machine learning; water consumption;All these keywords.
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