A Real-Time Data Analysis Platform for Short-Term Water Consumption Forecasting with Machine Learning

Boudhaouia, Aida; Wira, Patrice

doi:10.3390/forecast3040042

Cited by 17 publications

(9 citation statements)

References 23 publications

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“…For each value of the LC, std() represents the standard deviation. Additional tests can be run to determine if the instantaneous consumption is outside the bounds set by the lowest and highest LCs for the same day of the week and to spot any additional consumption that significantly deviates from the average consumption [32]. It is important to note that only data from water usage and a few statistical markers are used to identify abnormal and unexpected consumptions [32].…”

Section: Data Integrity and Preprocessingmentioning

confidence: 99%

“…Additional tests can be run to determine if the instantaneous consumption is outside the bounds set by the lowest and highest LCs for the same day of the week and to spot any additional consumption that significantly deviates from the average consumption [32]. It is important to note that only data from water usage and a few statistical markers are used to identify abnormal and unexpected consumptions [32]. Consumptions odd or unusual, are adjusted by interpolation during their duration and are not considered in the learning procedures.…”

Section: Data Integrity and Preprocessingmentioning

confidence: 99%

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Retracted: Real-Time Water and Electricity Consumption Monitoring Using Machine Learning Techniques

Bashir

2023

IEEE Access

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This work studies the automatic classification of water consumption patterns and electrical devices, both supervised and unsupervised. This involves training machine learning algorithms to identify normal and abnormal water consumption patterns and differentiate between different types of electrical devices. We performed an unsupervised classification of consumer water patterns in direct and indirect ways. The first is to use the raw consumption patterns obtained directly from the server. The second one corresponds to the use of the sampled consumption patterns. This classification is performed using hierarchical bottom-up classification and a self-organizing map. A probabilistic analysis of daily water consumption is performed to extract the percentage of daily consumption with the most information. It enables us to identify water consumption patterns more quickly by reducing the number of data points for each daily pattern, allowing us to recognize and classify anomalous behaviors as soon as feasible. Then, the signatures of electrical devices are classified using three ML algorithms: multilayer perceptron (MLP), k nearest neighbor (KNN), and decision tree (DT). Furthermore, assembly approaches are also studied. These are based on the OAA (One Against All) principle, which presents one class against all other classes, and the ECOC (Error-Correcting Output Codes) philosophy, which allows the classification error to be corrected. According to the bias/variance trade-off, both techniques enhanced classification by ensuring accuracy and generalization. A more in-depth analysis of the properties of the electrical devices is handled with an esemplastic approach based on gradient-boosting decision trees. The features of electrical devices can be extracted using this analysis based on the nature of their harmonic signatures.

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Section: Data Integrity and Preprocessingmentioning

confidence: 99%

Section: Data Integrity and Preprocessingmentioning

confidence: 99%

Retracted: Real-Time Water and Electricity Consumption Monitoring Using Machine Learning Techniques

Bashir

2023

IEEE Access

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“…Boudhaouia and Wira [119] evaluate the robustness of two prediction models for water consumption, which are based on machine learning techniques. Their study reveals the fact that long short-term memory (LSTM) has the lowest errors compared to back-propagation neural network (BPNN) and is able to detect the long-term dependencies between time steps of water consumption.…”

Section: Water Demand Forecastingmentioning

confidence: 99%

Advanced Techniques for Monitoring and Management of Urban Water Infrastructures—An Overview

Hângan

Chiru

Arsene

et al. 2022

Water

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Water supply systems are essential for a modern society. This article presents an overview of the latest research related to information and communication technology systems for water resource monitoring, control and management. The main objective of our review is to show how emerging technologies offer support for smart administration of water infrastructures. The paper covers research results related to smart cities, smart water monitoring, big data, data analysis and decision support. Our evaluation reveals that there are many possible solutions generated through combinations of advanced methods. Emerging technologies open new possibilities for including new functionalities such as social involvement in water resource management. This review offers support for researchers in the area of water monitoring and management to identify useful models and technologies for designing better solutions.

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“…The accuracy of the water demand prediction model is reflected in the data processing methods, such as Boudhaouia et al (Boudhaouia et al 2021), Mori Masaya et al (Masaya et al 2022), Yu et al (Jin-Won Yu et al 2022. The starting points of the improvement methods are different, all of which prove that the input and output play an important role in the prediction model, and the data factors that contain more original data information are screened out.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Urban Water Demand Based on Improved PCA-SSA-Elman Dynamic Neural Network

WANG

Zhao

et al. 2022

Preprint

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In principal component analysis, data logarithm transformation and row vector centralization improvement are carried out, reducing the input, and simplifying the network model. Use sparrow search algorithm for optimization, and compare the performance with cuckoo algorithm, seagull algorithm, and whale algorithm. To avoid problems as slow convergence speed and insufficient exploration ability, an improved sparrow search algorithm integrating refraction inverse learning mechanism, sin-cosine and Cauchy variation is proposed, a high-precision PCA-SCSSA-Elman neural network prediction model is established. The results show that SSA-Elman neural network has the fastest convergence speed and the optimal performance value is 0.1391 higher. Combined with the extracted principal component data, compared with PCA-Elman, PCA-SSA-Elman and PCA-SineSSA-Elman prediction models, the mean absolute error is higher than the other three models [45.6850%, 14.9168%, 11.7448%], the prediction error is minimal. The results show that the improved sparrow search algorithm can significantly improve the convergence speed and optimization accuracy.

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A Real-Time Data Analysis Platform for Short-Term Water Consumption Forecasting with Machine Learning

Cited by 17 publications

References 23 publications

Retracted: Real-Time Water and Electricity Consumption Monitoring Using Machine Learning Techniques

Retracted: Real-Time Water and Electricity Consumption Monitoring Using Machine Learning Techniques

Advanced Techniques for Monitoring and Management of Urban Water Infrastructures—An Overview

Prediction of Urban Water Demand Based on Improved PCA-SSA-Elman Dynamic Neural Network

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