A fully adaptive forecasting model for short-term drinking water demand

2013 ◽  
Vol 48 ◽  
pp. 141-151 ◽  
Author(s):  
M. Bakker ◽  
J.H.G. Vreeburg ◽  
K.M. van Schagen ◽  
L.C. Rietveld
Keyword(s):  
Drinking Water ◽  
Water Demand ◽  
Forecasting Model ◽  
Short Term ◽  
Fully Adaptive

scholarly journals Energetic optimization and evaluation of a drinking water pumping system: application at the Rassauta station

2018 ◽  
Vol 19 (2) ◽  
pp. 472-481 ◽  
Author(s):  
Bouach Ahcene ◽  
Benmamar Saadia
Keyword(s):  
Neural Networks ◽  
Drinking Water ◽  
Water Demand ◽  
Forecasting Model ◽  
Energy Losses ◽  
Pumping System ◽  
Water Pumping ◽  
Pumping Stations ◽  
Regulation Model ◽  
The City

Abstract The energy overconsumption at drinking-water pumping stations creates considerable energy losses. For this reason we have developed an NNGA tool of pumping management which optimizes the consumed energy by the pumping system with respect to the hydraulic functioning conditions in the distribution tank. This tool includes two models: a forecasting model for drinking water demand based on artificial neural networks and an optimization model using genetic algorithms. The results of the NNGA tool were compared with two pumping plans: the plan based on the pumping regulation model, and the plan used by the company of water and sewage of the city of Algiers. The analysis result was done with the help of performed indicators that we have developed and which enable the evaluation and diagnosis of the energetic function's system.

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

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 A Short-Term Water Demand Forecasting Model Using a Moving Window on Previously Observed Data

Water ◽  
2017 ◽  
Vol 9 (3) ◽  
pp. 172 ◽  
Author(s):  
Elena Pacchin ◽  
Stefano Alvisi ◽  
Marco Franchini
Keyword(s):  
Water Demand ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Moving Window ◽  
Short Term ◽  
Water Demand Forecasting ◽  
Demand Forecasting Model

Method for estimating network structure of short-term traffic flow forecasting model

2010 ◽  
Vol 29 (12) ◽  
pp. 3249-3252
Author(s):  
Ying XIA ◽  
Zhong-jun LIANG
Keyword(s):  
Traffic Flow ◽  
Network Structure ◽  
Forecasting Model ◽  
Short Term ◽  
Traffic Flow Forecasting

scholarly journals Usage of the Pareto Fronts as a Tool to Select Data in the Forecasting Process—A Short-Term Electric Energy Demand Forecasting Case

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3204
Author(s):  
Michał Sabat ◽  
Dariusz Baczyński
Keyword(s):  
Energy Demand ◽  
Demand Forecasting ◽  
Electric Energy ◽  
Forecasting Model ◽  
Short Term ◽  
Complex Evaluation ◽  
Proposed Model ◽  
Energy Demand Forecasting ◽  
Pareto Fronts ◽  
Naive Approach

Transmission, distribution, and micro-grid system operators are struggling with the increasing number of renewables and the changing nature of energy demand. This necessitates the use of prognostic methods based on ever shorter time series. This study depicted an attempt to develop an appropriate method by introducing a novel forecasting model based on the idea to use the Pareto fronts as a tool to select data in the forecasting process. The proposed model was implemented to forecast short-term electric energy demand in Poland using historical hourly demand values from Polish TSO. The study rather intended on implementing the range of different approaches—scenarios of Pareto fronts usage than on a complex evaluation of the obtained results. However, performance of proposed models was compared with a few benchmark forecasting models, including naïve approach, SARIMAX, kNN, and regression. For two scenarios, it has outperformed all other models by minimum 7.7%.

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