Short term water demand forecasting using regional data
作者： Tugba Zeynep Yildiz , Tevfik Aytekin
关键词:
摘要: Limited water resources and changing climatic conditions make one of the critical natural resources. In order to manage this limited resource in most effective way, real-time monitoring automatic control systems are becoming increasingly popular. Water demand forecasting is important subjects these studies. Accurate increases efficiency management networks also allows for leak/fraud detection. work, we carry out short term using consumption data collected from meters a regional area. For forecasting, first clean data, extract various features apply machine learning methods forecasting. After giving experimental results discuss future improvements.
sci-hub.se 参考文章(10)
Diego García Valverde, Joseba Jokin Quevedo Casín, Vicenç Puig Cayuela, Jordi Saludes Closa, None, Water demand estimation and outlier detection from smart meter data using classification and Big Data methods 2nd New Developments in IT & Water Conference, 8-10 February 2015, Rotterdam (Holland). pp. 1- 8 ,(2015)
Diego Garcia, Joseba Quevedo, Vicenç Puig, Jordi Saludes, Santiago Espin, Jaume Roquet, Fernando Valero, Automatic Validation of Flowmeter Data in Transport Water Networks: Application to the ATLLc Water Network Intelligent Data Engineering and Automated Learning – IDEAL 2014. pp. 118- 125 ,(2014) , 10.1007/978-3-319-10840-7_15
David Walker, Enrico Creaco, Lydia Vamvakeridou-Lyroudia, Raziyeh Farmani, Zoran Kapelan, Dragan Savić, Forecasting Domestic Water Consumption from Smart Meter Readings Using Statistical Methods and Artificial Neural Networks Procedia Engineering. ,vol. 119, pp. 1419- 1428 ,(2015) , 10.1016/J.PROENG.2015.08.1002
Yun Bai, Pu Wang, Chuan Li, Jingjing Xie, Yin Wang, A multi-scale relevance vector regression approach for daily urban water demand forecasting Journal of Hydrology. ,vol. 517, pp. 236- 245 ,(2014) , 10.1016/J.JHYDROL.2014.05.033
J. Quevedo, V. Puig, G. Cembrano, J. Blanch, J. Aguilar, D. Saporta, G. Benito, M. Hedo, A. Molina, Validation and reconstruction of flow meter data in the Barcelona water distribution network Control Engineering Practice. ,vol. 18, pp. 640- 651 ,(2010) , 10.1016/J.CONENGPRAC.2010.03.003
Muhammad A. Al-Zahrani, Amin Abo-Monasar, Urban Residential Water Demand Prediction Based on Artificial Neural Networks and Time Series Models Water Resources Management. ,vol. 29, pp. 3651- 3662 ,(2015) , 10.1007/S11269-015-1021-Z
Tingting Fang, Risto Lahdelma, Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system Applied Energy. ,vol. 179, pp. 544- 552 ,(2016) , 10.1016/J.APENERGY.2016.06.133
Maamar Sebri, Forecasting urban water demand: A meta-regression analysis. Journal of Environmental Management. ,vol. 183, pp. 777- 785 ,(2016) , 10.1016/J.JENVMAN.2016.09.032
Jordi Saludes, Joseba Quevedo, Vicenç Puig, Demand Forecasting for Real-Time Operational Control Springer, Cham. pp. 99- 111 ,(2017) , 10.1007/978-3-319-50751-4_6
Weicong Kong, Zhao Yang Dong, Youwei Jia, David J. Hill, Yan Xu, Yuan Zhang, Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network IEEE Transactions on Smart Grid. ,vol. 10, pp. 841- 851 ,(2019) , 10.1109/TSG.2017.2753802