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

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

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

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.

Application of short-term water demand prediction model to Seoul

2002 ◽  
Vol 46 (6-7) ◽  
pp. 255-261 ◽  
Author(s):  
C.N. Joo ◽  
J.Y. Koo ◽  
M.J. Yu
Keyword(s):  
Water Demand ◽  
Model Building ◽  
Time Series Prediction ◽  
Social Factor ◽  
Maximum Temperature ◽  
Factors Affecting ◽  
Demand Prediction ◽  
Daily Water ◽  
Water Demand Prediction ◽  
Artificial Neural Network Ann

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.

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

Water ◽  
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.

scholarly journals A Two-Layer Water Demand Prediction System in Urban Areas Based on Micro-Services and LSTM Neural Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 147647-147661
Author(s):  
Ahmed Abdel Nasser ◽  
Magdi Z. Rashad ◽  
Sherif E. Hussein
Keyword(s):  
Neural Networks ◽  
Water Demand ◽  
Urban Areas ◽  
Prediction System ◽  
Demand Prediction ◽  
Water Demand Prediction

Hybrid Approach for Water Demand Prediction Based on Fuzzy Congnitive Maps

2017 ◽  
Vol 8 (2) ◽  
pp. 567
Author(s):  
G. Jenitha
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Time Series Prediction ◽  
Hybrid Approach ◽  
Structure Optimization ◽  
Fuzzy Cognitive Maps ◽  
Demand Prediction ◽  
Artificial Neural ◽  
Future Work

<p> In this study, we propose a new hybrid approach for time series prediction based on the efficient capabilities of fuzzy cognitive maps (FCMs) with structure optimization algorithms and artificial neural networks (ANNs). The proposed structure optimization genetic algorithm (SOGA) for automatic construction of FCM is used for modeling complexity based on historical time series, and artificial neural networks (ANNs) which are used at the final process for making time series prediction. The suggested SOGA-FCM method is used for selecting the most important nodes (attributes) and interconnections among them which in the next stage are used as the input data to ANN used for time series prediction after training. The FCM with proficient learning calculations and ANN have been as of now demonstrated as adequate strategies for setting aside a few minutes arrangement anticipating. The execution of the proposed approach is exhibited through the examination of genuine information of every day water request and the comparing expectation. The multivariate examination of recorded information is held for nine factors, season, month, day or week, occasion, mean and high temperature, rain normal, touristic action and water request. The entire approach was actualized in a clever programming device at first sent for FCM forecast. Through the exploratory investigation, the value of the new mixture approach in water request forecast is illustrated, by computing the mean outright blunder (as one of the outstanding expectation measures). The outcomes are promising for future work to this bearing.</p>

scholarly journals Uncertain time series forecasting method for the water demand prediction in Beijing

2021 ◽  
Author(s):  
Haiyan Li ◽  
Xiaosheng Wang ◽  
Haiying Guo
Keyword(s):  
Time Series ◽  
Water Demand ◽  
Autoregressive Model ◽  
Performance Criteria ◽  
Water Supply Systems ◽  
Identification Algorithm ◽  
Demand Prediction ◽  
Uncertain Variables ◽  
Water Demand Prediction ◽  
Uncertain Time

Abstract Water demand prediction is crucial for effectively planning and management of water supply systems to handle the problem of water scarcity. Taking into account the uncertainties and imprecisions within the framework of water demand forecasting, the uncertain time series prediction method is introduced for the water demand prediction. Uncertain time series is a sequence of imprecisely observed values that are characterized by uncertain variables and the corresponding uncertain autoregressive model is employed to describe it for predicting the future values. The main contributions of this paper are shown as follows. Firstly, by defining the auto-similarity of uncertain time series, the identification algorithm of uncertain autoregressive model order is proposed. Secondly, a new parameter estimation method based on the uncertain programming is developed. Thirdly, the imprecisely observed values are assumed as the linear uncertain variables and a ratio-based method is presented for constructing the uncertain time series. Finally, the proposed methodologies are applied to model and forecast the Beijing's water demand under different confidence levels and compared with the traditional time series, i.e., ARIMA method. The experimental results are evaluated on the basis of performance criteria, which shows that the proposed method outperforms over the ARIMA method for water demand prediction.

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