Computational Intelligence in Smart water and gas grids: An up-to-date overview

Fagiani, Marco; Squartini, Stefano; Gabrielli, Leonardo; Pizzichini, Mirco; Spinsante, Susanna

doi:10.1109/ijcnn.2014.6889603

Cited by 13 publications

(9 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This motivated the work by Fagiani et al [6] which is presented here in an up-to-date extension, from 2009 to date. A comprehensive collection of recent state-of-the-art works, concerning water and natural gas forecasting, and related datasets are presented.…”

Section: Introductionmentioning

confidence: 68%

“…Specifically, in the first part of the paper an update of the state-of-the-art information on datasets and load forecasting techniques for natural gas and water previously collected by Fagiani et al [6] has been presented. The available datasets have been gathered and, despite some shortcomings, used for the experimental phase.…”

Section: Resultsmentioning

confidence: 99%

“…The ESNs, adapting the ESN MATLAB s code, 6 have been tested by changing the reservoir dimension, the value of the initial transient, the leaking rate (α) and the regularization coefficient (β). Each parameter has been changed according to the Lukoševi-čius guide [44], and specifically the adopted values are β ¼ 1; 10 À 1 ; 10 À 2 ; 10 À 3 ; …; 10 À 15 ; 0 n o :…”

Section: Echo State Networkmentioning

confidence: 99%

See 2 more Smart Citations

A review of datasets and load forecasting techniques for smart natural gas and water grids: Analysis and experiments

et al. 2015

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 68%

Section: Resultsmentioning

confidence: 99%

Section: Echo State Networkmentioning

confidence: 99%

See 1 more Smart Citation

A review of datasets and load forecasting techniques for smart natural gas and water grids: Analysis and experiments

et al. 2015

View full text Add to dashboard Cite

“…Several studies have been published in the electrical energy field, mostly facilitated by the availability of suitable databases [10]. Different is the case for research on the analysis of gas demand, especially when it comes to clustering and consumer profiling based on consumption data.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Residential Gas Consumers Using Clustering Analysis

et al. 2017

View full text Add to dashboard Cite

Abstract:The growing environmental concerns and liberalization of energy markets have resulted in an increased competition between utilities and a strong focus on efficiency. To develop new energy efficiency measures and optimize operations, utilities seek new market-related insights and customer engagement strategies. This paper proposes a clustering-based methodology to define the segmentation of residential gas consumers. The segments of gas consumers are obtained through a detailed clustering analysis using smart metering data. Insights are derived from the segmentation, where the segments result from the clustering process and are characterized based on the consumption profiles, as well as according to information regarding consumers' socio-economic and household key features. The study is based on a sample of approximately one thousand households over one year. The representative load profiles of consumers are essentially characterized by two evident consumption peaks, one in the morning and the other in the evening, and an off-peak consumption. Significant insights can be derived from this methodology regarding typical consumption curves of the different segments of consumers in the population. This knowledge can assist energy utilities and policy makers in the development of consumer engagement strategies, demand forecasting tools and in the design of more sophisticated tariff systems.

show abstract

“…water networks) have not received as much attention [1]. As a result, algorithmic solutions for water and gas networks are still at an early stage compared to their smart grid counterpart.…”

Section: Introductionmentioning

confidence: 99%

Constrained LMS for dynamic flow networks

Eftaxias

Took

Venturini³

et al. 2017

2017 International Joint Conference on Neural Networks (IJCNN)

View full text Add to dashboard Cite

Abstract-In this era of climate change, there is a growing need to offer adaptive learning algorithms in the optimisation of natural resources. These resources are typically optimised by evolutionary algorithms. However, evolutionary algorithms (EAs) are no longer adequate due to the 'drift' component introduced by environmental factors such as flash flooding. We therefore propose a novel constrained Least Mean Squares (LMS) algorithm for the optimisation of flow networks. For rigor, we provide a stability analysis of our adaptive algorithm, which enables us to interpret the physical meaning of the network at equilibrium. We evaluate our proposed method against genetic algorithm (GA), the most common evolutionary algorithm. The results are promising: not only the proposed constrained LMS has a performance advantage over GA, but its computational cost is significantly lower making it more suitable for real-time applications.

show abstract

Computational Intelligence in Smart water and gas grids: An up-to-date overview

Cited by 13 publications

References 20 publications

A review of datasets and load forecasting techniques for smart natural gas and water grids: Analysis and experiments

A review of datasets and load forecasting techniques for smart natural gas and water grids: Analysis and experiments

Segmentation of Residential Gas Consumers Using Clustering Analysis

Constrained LMS for dynamic flow networks

Contact Info

Product

Resources

About