Application of short-term water demand prediction model to Seoul

To predict daily water demand for Seoul, Korea, the artificial neural network (ANN) was used. For the cross correlation, the factors affecting water demand such as maximum temperature, humidity, and wind speed as natural factors, holidays as a social factor and daily demand 1 day before were used. From the results of learning using various hidden layers and units in order to establish the structure of optimal ANN, the case of 3 hidden layers and numbers of unit with the same number of input factors showed the best result and, therefore, it was applied to seasonal water demand prediction. The performance of ANN was compared with a multiple regression method. We discuss the representation ability of the model building process and the applicability of the ANN approach for the daily water demand prediction. ANN provided reasonable results for time series prediction.

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Proportional integral derivative booster for neural networks-based time-series prediction: Case of water demand prediction

Engineering Applications of Artificial Intelligence ◽

10.1016/j.engappai.2021.104570 ◽

2022 ◽

Vol 108 ◽

pp. 104570

Author(s):

Tony Salloom ◽

Okyay Kaynak ◽

Xinbo Yu ◽

Wei He

Keyword(s):

Neural Networks ◽

Time Series ◽

Water Demand ◽

Time Series Prediction ◽

Proportional Integral Derivative ◽

Proportional Integral ◽

Demand Prediction ◽

Water Demand Prediction

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Multi-Model Coupling Water Demand Prediction Optimization Method for Megacities Based on Time Series Decomposition

10.21203/rs.3.rs-406939/v1 ◽

2021 ◽

Author(s):

Xin Liu ◽

Xuefeng Sang ◽

Jiaxuan Chang ◽

Yang Zheng

Keyword(s):

Time Series ◽

Water Supply ◽

Water Demand ◽

Coupling Model ◽

T Test ◽

Demand Prediction ◽

Interval Prediction ◽

Time Series Decomposition ◽

Daily Water ◽

Water Demand Prediction

Abstract The fluctuation of water supply is affected by the living habits and population mobility, so the daily water supply is significantly non-stationarity, which presents a great challenge to the water demand prediction based on data-driven model. To solve this problem, the Hodrick-Prescott (HP) and wavelet transform (WT) time series decomposition methods, and ensemble learning (EL) were introduced, coupling model bidirectional long short term memory (BLSTM), seasonal autoregressive integrated moving average (SARIMA) and Gaussian radial basis function neural network (GRBFNN) were developed, and interval prediction was carried out based on student's t-test (T-test). This research method was applied to the daily water demand prediction in Shenzhen and cross-validation was performed. It is found that the decomposed subseries has obvious law, and WT is superior to HP decomposition method. However, the maximum decomposition level (MDL) of WT should not be set too high, otherwise the trend characteristics of subseries will be weakened. The results show that the potential characteristics and quantitative relationships of historical data can be learned accurately based on WT and coupling model. Although the corona virus disease 2019 (COVID-19) outbreak in 2020 caused a variation in water supply law, this variation is still within the interval prediction. The WT and coupling model satisfactorily predicted water demand and provided the lowest mean square error (0.17%), mean relative error (0.1) and mean absolute error (3.32%) and the highest Nash-Sutcliffe efficiency (97.21%) and correlation coefficient (0.99) in testing set.

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Application of LSTM Networks for Water Demand Prediction in Optimal Pump Control

Water ◽

10.3390/w13050644 ◽

2021 ◽

Vol 13 (5) ◽

pp. 644

Author(s):

Christian Kühnert ◽

Naga Mamatha Gonuguntla ◽

Helene Krieg ◽

Dimitri Nowak ◽

Jorge A. Thomas

Keyword(s):

Drinking Water ◽

Water Demand ◽

Water Distribution ◽

Short Term Memory ◽

Time Series Prediction ◽

Real Life ◽

Training Data ◽

Demand Prediction ◽

Additional Information ◽

Water Demand Prediction

Every morning, water suppliers need to define their pump schedules for the next 24 h for drinking water production. Plans must be designed in such a way that drinking water is always available and the amount of unused drinking water pumped into the network is reduced. Therefore, operators must accurately estimate the next day’s water consumption profile. In real-life applications with standard consumption profiles, some expert system or vector autoregressive models are used. Still, in recent years, significant improvements for time series prediction have been achieved through special deep learning algorithms called long short-term memory (LSTM) networks. This paper investigates the applicability of LSTM models for water demand prediction and optimal pump control and compares LSTMs against other methods currently used by water suppliers. It is shown that LSTMs outperform other methods since they can easily integrate additional information like the day of the week or national holidays. Furthermore, the online- and transfer-learning capabilities of the LSTMs are investigated. It is shown that LSTMs only need a couple of days of training data to achieve reasonable results. As the focus of the paper is on the real-world application of LSTMs, data from two different water distribution plants are used for benchmarking. Finally, it is shown that the LSTMs significantly outperform the system currently in operation.

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Multi-Model Coupling Water Demand Prediction Optimization Method for Megacities Based on Time Series Decomposition

Water Resources Management ◽

10.1007/s11269-021-02927-y ◽

2021 ◽

Author(s):

Xin Liu ◽

Xuefeng Sang ◽

Jiaxuan Chang ◽

Yang Zheng

Keyword(s):

Water Supply ◽

Water Demand ◽

Short Term Memory ◽

Prediction Interval ◽

Virus Disease ◽

Generalization Ability ◽

Demand Prediction ◽

Daily Data ◽

Daily Water ◽

Water Demand Prediction

AbstractThe water supply in megacities can be affected by the living habits and population mobility, so the fluctuation degree of daily water supply data is acute, which presents a great challenge to the water demand prediction. This is because that non-stationarity of daily data can have a large influence on the generalization ability of models. In this study, the Hodrick-Prescott (HP) and wavelet transform (WT) methods were used to carry out decomposition of daily data to solve the non-stationarity problem. The bidirectional long short term memory (BLSTM), seasonal autoregressive integrated moving average (SARIMA) and Gaussian radial basis function neural network (GRBFNN) were developed to carry out prediction of different subseries. The ensemble learning was introduced to improve the generalization ability of models, and prediction interval was generated based on student's t-test to cope with the variation of water supply laws. This study method was applied to the daily water demand prediction in Shenzhen and cross-validation was performed. The results show that WT is superior to HP decomposition method, but maximum decomposition level of WT should not be set too high, otherwise the trend characteristics of subseries will be weakened. Although the corona virus disease 2019 (COVID-19) outbreak caused a variation in water supply laws, this variation is still within the prediction interval. The WT and coupling models accurately predict water demand and provide the optimal mean square error (0.17%), Nash-Sutcliffe efficiency (97.21%), mean relative error (0.1), mean absolute error (3.32%), and correlation coefficient (0.99).

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