ANN Daily Peak Forecast for Peak Demand Charges Management

Kadri, Abdeslem; Mohammadi, Fazel

doi:10.1109/ccece47787.2020.9255778

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…This value has been determined by forecasting with ANN. ANN was selected for it's efficiency in multivariate forecasting [38], [39]. The fuzzy rule base of the system has been developed after extensive study of the data provided by surveys over multiple cities and towns spread over different regions [40], [41].…”

Section: Model Of Switching Schedule Of Loadsmentioning

confidence: 99%

Development of regional load management system based on rural, semi urban and urban loads-a critical analysis

Ganguly

Sil

2022

Bulletin EEI

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The sharp rise in population during the last half century has created immense pressure on the resources required for generation of energy essential to lead a comfortable and healthy lifestyle. The drive towards 100% electrification in developing countries like India has also contributed to this increase in demand. Till recently, fossil fuel was use to supply the bulk of this power. Now, the world is moving more and more towards renewable energy. This paper presents a model where several regions are combined together based on the demand profile of the regions segregated as urban, semi urban and rural along with the flexibility to schedule loads on the basis of availability of renewable energy sources within the area of the regions. The main focus is on detailed neural-networking based load forecasting and developing a load management system to manage load based on availability of distributed generation capacity and available tariff system. A solution is proposed in this paper based on a new approach to answer load management on the basis of region, population demographics and per capita energy consumption. A considerable amount of improvement to manage demand is intended to be attained and has been demonstrated in this research work.

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Section: Model Of Switching Schedule Of Loadsmentioning

confidence: 99%

Development of regional load management system based on rural, semi urban and urban loads-a critical analysis

Ganguly

Sil

2022

Bulletin EEI

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“…Other fields of application of ANN are image processing [15], finance [3], management [9], education, engineering, trading.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Fault Detection in Three-phase Induction Motor based on Data Acquisition and ANN based Data Processing

Moldovan

Ghincu

Moldovan

et al. 2022

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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The main objective of this paper is to investigate how a failure in the functioning of a normal electrical system represented by a three-phase asynchronous motor will modify the voltages and currents present in the system and if it is possible to design a system that is able to automatically detect the fault, based on the use of modern data acquisition system and powerful computer processing capabilities. The detection of faulty signals is realised using Feedforward Artificial Neural Networks.

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ANN Daily Peak Forecast for Peak Demand Charges Management

Cited by 2 publications

References 17 publications

Development of regional load management system based on rural, semi urban and urban loads-a critical analysis

Development of regional load management system based on rural, semi urban and urban loads-a critical analysis

Fault Detection in Three-phase Induction Motor based on Data Acquisition and ANN based Data Processing

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