Application of Mamdani model-based fuzzy inference system in water consumption estimation using time series

Surendra, H. J.; Deka, Paresh Chandra; Rajakumara, H. N.

doi:10.1007/s00500-022-06966-4

Cited by 5 publications

(1 citation statement)

References 14 publications

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“…Along with statistical methods of data analysis, artificial intelligence methods are increasingly being used. Artificial intelligence technologies allow solving problems of classification, clustering, optimization, forecasting, and management [15]. The article [16] uses deep learning models of neural networks.…”

Section: Introductionmentioning

confidence: 99%

Water supply telemetry data processing in apartment buildings

Ignatova

2024

BIO Web Conf.

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The digital transformation of construction and housing and utilities sector involves data-based management. The most accessible data is telemetry of electricity, gas, heat and water consumption. Despite the fact that not all apartment buildings are equipped with meters yet, it is necessary to think about methods for processing measurement data. Intelligent data processing methods are gaining wide popularity. The purpose of this study is the processing and analysis of water consumption telemetry data. The objective of the study is to determine the periods of nighttime water consumption using the clustering method. The data of apartment meters of hourly consumption of hot and cold water are investigated. The measurement period is 1 month. Intelligent cluster analysis was conducted based on the DBSCAN machine learning model (Density-based spatial clustering of applications with noise). Clustering objects are the hours of the day. As a result of the study, the hours of night consumption of cold and hot water were allocated, both in the whole month and separately on weekdays and Sundays. The conclusion is made about the benefits of using intelligent cluster analysis of water consumption telemetry data for effective management of water resources and equipment.

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Section: Introductionmentioning

confidence: 99%

Water supply telemetry data processing in apartment buildings

Ignatova

2024

BIO Web Conf.

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Artificial Intelligence for Water Consumption Assessment: State of the Art Review

Morain,

Ilangovan,

Delhom

et al. 2024

Water Resour Manage

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In recent decades, demand for freshwater resources has increased the risk of severe water stress. With the growing prevalence of artificial intelligence (AI), many researchers have turned to it as an alternative to linear methods to assess water consumption (WC). Using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, this study utilized 229 screened publications identified through database searches and snowball sampling. This study introduces novel aspects of AI's role in water consumption assessment by focusing on innovation, application sectors, sustainability, and machine learning applications. It also categorizes existing models, such as standalone and hybrid, based on input, output variables, and time horizons. Additionally, it classifies learnable parameters and performance indexes while discussing AI models' advantages, disadvantages, and challenges. The study translates this information into a guide for selecting AI models for WC assessment. As no one-size-fits-all AI model exists, this study suggests utilizing hybrid AI models as alternatives. These models offer flexibility regarding efficiency, accuracy, interpretability, adaptability, and data requirements. They can address the limitations of individual models, leverage the strengths of different approaches, and provide a better understanding of the relationships between variables. Several knowledge gaps were identified, resulting in suggestions for future research.

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