scholarly journals Hybrid Model for Short-Term Water Demand Forecasting Based on Error Correction Using Chaotic Time Series

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1683
Author(s):  
Shan Wu ◽  
Hongquan Han ◽  
Benwei Hou ◽  
Kegong Diao
Keyword(s):  
Time Series ◽  
Error Correction ◽  
Hybrid Model ◽  
Water Demand ◽  
Distribution Systems ◽  
Demand Forecasting ◽  
Chaotic Time Series ◽  
Forecasting Model ◽  
Short Term ◽  
Water Demand Forecasting

Short-term water demand forecasting plays an important role in smart management and real-time simulation of water distribution systems (WDSs). This paper proposes a hybrid model for the short-term forecasting in the horizon of one day with 15 min time steps, which improves the forecasting accuracy by adding an error correction module to the initial forecasting model. The initial forecasting model is firstly established based on the least square support vector machine (LSSVM), the errors time series obtained by comparing the observed values and the initial forecasted values is next transformed into chaotic time series, and then the error correction model is established by the LSSVM method to forecast errors at the next time step. The hybrid model is tested on three real-world district metering areas (DMAs) in Beijing, China, with different demand patterns. The results show that, with the help of the error correction module, the hybrid model reduced the mean absolute percentage error (MAPE) of forecasted demand from (5.64%, 4.06%, 5.84%) to (4.84%, 3.15%, 3.47%) for the three DMAs, compared with using LSSVM without error correction. Therefore, the proposed hybrid model provides a better solution for short-term water demand forecasting on the tested cases.

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 A Probabilistic Short-Term Water Demand Forecasting Model Based on the Markov Chain

Water ◽  
2017 ◽  
Vol 9 (7) ◽  
pp. 507 ◽  
Author(s):  
Francesca Gagliardi ◽  
Stefano Alvisi ◽  
Zoran Kapelan ◽  
Marco Franchini
Keyword(s):  
Markov Chain ◽  
Water Demand ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Short Term ◽  
Water Demand Forecasting ◽  
Model Based ◽  
Demand Forecasting Model

Application of Time Series-Exponential Smoothing Model on Urban Water Demand Forecasting

2011 ◽  
Vol 183-185 ◽  
pp. 1158-1162 ◽  
Author(s):  
Jun Liang Liu ◽  
Xu Chen ◽  
Tie Jian Zhang
Keyword(s):  
Time Series ◽  
Water Demand ◽  
Demand Forecasting ◽  
Original Data ◽  
Exponential Smoothing ◽  
Forecasting Model ◽  
Urban Water ◽  
Analysis Software ◽  
Urban Water Demand ◽  
Statistical Analysis Software

Based on the traditional time series methods, this paper researched a time series-exponential smoothing model that is built by SPSS statistical analysis software. In the application of the model, the original data of water consumption were in processed by a particular smoothing method first.Secondly, the processed data were used to build a time series-exponential smoothing model. On error test, we found that this forecasting model has advantages of better effect, high precision and minor error on urban water demand forecasing.

scholarly journals Implementation of Water Demand Forecasting Model to Aid Sustainable Water Supply Chain Management in UAE

2020 ◽  
Author(s):  
Vian Ahmed ◽  
Ahmad Saad ◽  
Hasan Saleh ◽  
Sara Saboor ◽  
Nikita Kasianov ◽  
...  
Keyword(s):  
Water Resources ◽  
United Arab Emirates ◽  
Water Scarcity ◽  
Water Demand ◽  
Short Term Memory ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Water Demand Forecasting ◽  
Sustainable Water Supply ◽  
The Future

Climate change has become the greatest threat to the survival of world and its ecosystem. With the irreversible impact on the ecosystem, problems like rise in sea level, food-insecurity, natural resources scarcity, seasonal disorders have increased over the past few years. Among these problems, the issue of water scarcity due to the lack of water resources and global warming has plagued several nations. Owing to the rising concerns over water scarcity United Nations (UN) has acknowledged water as a primary resource to the development of societies under the ‘Water Goal’ of the sustainable development goals. As the changing climate and intermittent availability of water resources pose major challenges to forecast demand, especially in countries like the United Arab Emirates (UAE) which has one of the highest per capita residential water consumption rates in the world. Therefore, the aim of this study is to propose an accurate water demand forecasting technique that incorporates all significant factors to predict the future water demands of the UAE. The forecasting model used is the Long Short Term Memory (LSTM), with the factors considered are mean temperature, mean rainfall, relative humidity, Gross Domestic Product (GDP), Consumer Price Index (CPI) and population growth. The LSTM model predicts the water demand forecasting in the UAE showing that the future demand will decrease from 1821 million m3 in 2018 to 1809.9 million m3 in 2027.

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