LAYERED MACHINE LEARNING FOR SHORT-TERM WATER DEMAND FORECASTING

2015 ◽  
Vol 14 (9) ◽  
pp. 2061-2072
Author(s):  
Antonio Candelieri ◽  
Davide Soldi ◽  
Francesco Archetti
Keyword(s):  
Machine Learning ◽  
Water Demand ◽  
Demand Forecasting ◽  
Short Term ◽  
Water Demand Forecasting

scholarly journals Short-term water demand forecasting using hybrid supervised and unsupervised machine learning model

Smart Water ◽  
2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Mo’tamad Bata ◽  
Rupp Carriveau ◽  
David S.-K. Ting
Keyword(s):  
Machine Learning ◽  
Water Demand ◽  
Demand Forecasting ◽  
Hybrid Models ◽  
Supervised Machine Learning ◽  
Forecasting Model ◽  
Short Term ◽  
Unsupervised Machine Learning ◽  
Water Demand Forecasting ◽  
Clustering Model

Abstract Regression Tree (RT) forecasting models are widely used in short-term demand forecasting. Likewise, Self-Organizing Maps (SOM) models are known for their ability to cluster and organize unlabeled big data. Herein, a combination of these two Machine Learning (ML) techniques is proposed and compared to a standalone RT and a Seasonal Autoregressive Integrated Moving Average (SARIMA) models, in forecasting the short-term water demand of a municipality. The inclusion of the Unsupervised Machine Learning clustering model has resulted in a significant improvement in the performance of the Supervised Machine Learning forecasting model. The results show that using the output of the SOM clustering model as an input for the RT forecasting model can, on average, double the accuracy of water demand forecasting. The Mean Absolute Percentage Error (MAPE) and the Normalized Root Mean Squared Error (NRMSE) were calculated for the proposed models forecasting 1 h, 8 h, 24 h, and 7 days ahead. The results show that the hybrid models outperformed the standalone RT model, and the broadly used SARIMA model. On average, hybrid models achieved double accuracy in all 4 forecast periodicities. The increase in forecasting accuracy afforded by this hybridized modeling approach is encouraging. In our application, it shows promises for more efficient energy and water management at the water utilities.

scholarly journals Short-term water demand forecasting using machine learning techniques

2018 ◽  
Vol 20 (6) ◽  
pp. 1343-1366 ◽  
Author(s):  
A. Antunes ◽  
A. Andrade-Campos ◽  
A. Sardinha-Lourenço ◽  
M. S. Oliveira
Keyword(s):  
Machine Learning ◽  
Water Demand ◽  
Demand Forecasting ◽  
Maximum Level ◽  
Water Utilities ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Water Demand Forecasting ◽  
Learning Techniques

Abstract Nowadays, a large number of water utilities still manage their operation on the instant water demand of the network, meaning that the use of the equipment is conditioned by the immediate water necessity. The water reservoirs of the networks are filled using pumps that start working when the water level reaches a specified minimum, stopping when it reaches a maximum level. Shifting the focus to water management based on future demand allows use of the equipment when energy is cheaper, taking advantage of the electricity tariff in action, thus bringing significant financial savings over time. Short-term water demand forecasting is a crucial step to support decision making regarding the equipment operation management. For this purpose, forecasting methodologies are analyzed and implemented. Several machine learning methods, such as neural networks, random forests, support vector machines and k-nearest neighbors, are evaluated using real data from two Portuguese water utilities. Moreover, the influence of factors such as weather, seasonality, amount of data used in training and forecast window is also analysed. A weighted parallel strategy that gathers the advantages of the different machine learning techniques is suggested. The results are validated and compared with those achieved by autoregressive integrated moving average (ARIMA) also using benchmarks.

scholarly journals Applying human mobility and water consumption data for short-term water demand forecasting using classical and machine learning models

2020 ◽  
Vol 17 (1) ◽  
pp. 32-42 ◽  
Author(s):  
Kamil Smolak ◽  
Barbara Kasieczka ◽  
Wieslaw Fialkiewicz ◽  
Witold Rohm ◽  
Katarzyna Siła-Nowicka ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Consumption ◽  
Water Demand ◽  
Human Mobility ◽  
Demand Forecasting ◽  
Learning Models ◽  
Short Term ◽  
Water Demand Forecasting ◽  
Consumption Data ◽  
Machine Learning Models

scholarly journals Short-Term Water Demand Forecasting Algorithm Using AR Model and MLP

2009 ◽  
Vol 19 (5) ◽  
pp. 713-719 ◽  
Author(s):  
Gee-Seon Choi ◽  
Chool Yu ◽  
Ryuk-Min Jin ◽  
Seong-Keun Yu ◽  
Myung-Geun Chun
Keyword(s):  
Water Demand ◽  
Demand Forecasting ◽  
Ar Model ◽  
Short Term ◽  
Water Demand Forecasting

scholarly journals Evaluation of short-term water demand forecasting using ensemble model

2014 ◽  
Vol 28 (4) ◽  
pp. 377-389
Author(s):  
Byung-Jin So ◽  
Hyun-Han Kwon ◽  
Ja-Young Gu ◽  
Bong-Kil Na ◽  
Byung-Seop Kim
Keyword(s):  
Water Demand ◽  
Demand Forecasting ◽  
Ensemble Model ◽  
Short Term ◽  
Water Demand Forecasting

A Comparison of Short-Term Water Demand Forecasting Models

2019 ◽  
Vol 33 (4) ◽  
pp. 1481-1497 ◽  
Author(s):  
E. Pacchin ◽  
F. Gagliardi ◽  
S. Alvisi ◽  
M. Franchini
Keyword(s):  
Water Demand ◽  
Demand Forecasting ◽  
Short Term ◽  
Forecasting Models ◽  
Water Demand Forecasting
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