Selection method of community load coincidence factor based on BP neural network

Liu, Xinyuan; Song, Shuyong; Zheng, Haibin; Fu, Guoyun

doi:10.1109/iccsnt.2013.6967157

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…The monthly energy consumption in MWh (units sent) for each feeder is available in the grid station log sheets and can be computed by subtracting the previous month's consumption value from the present month. The WAPDA is using the coincidence factor [19] for forecasting the distribution planning and in this study; a value of 0.9 will be used as a coincidence factor. The coincidence factor (CF) is the ratio of grid station peak demand to the sum of individual demands within a specific period.…”

Section: Losses Estimation and Calculationmentioning

confidence: 99%

Reduction of distribution system losses through WAPDA distribution system line- loss reduction program

Asghar¹,

Ullah²,

Ullah³

et al. 2022

murjet

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The losses in the power system vary the gap between energy supply and demand. Among these losses, the distribution system has major contributions compared to the transmission system. This paper thoroughly examines the PESCO distribution losses and implements a practical approach that forties in the loss’s minimization. Using the bifurcation and restoration strategies, the PESCO distribution system can be well-organized and an effective system can be designed. The proposed approach also helps in estimating the entailed capital investments and the rate of return over time. Both the HT (High Tension) and LT (Low Tension) distribution systems are considered for observation and the proposed systems are endorsed. Computer-based software, such as ELR (Energy Loss Reduction) and CADPAW (Computer-Aided Distribution Planning Work) is exploited to measure the results of both the proposed and existing system. After the restoration of a particular system, recommendations are concluded. The proposed approach applies to any existing power system.

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Section: Losses Estimation and Calculationmentioning

confidence: 99%

Reduction of distribution system losses through WAPDA distribution system line- loss reduction program

Asghar¹,

Ullah²,

Ullah³

et al. 2022

murjet

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“…In [9] the concept of a perceptron-type multilayer neural network for the estimation of coincidence factors values was presented. The network requires information about 10 issues influencing the factor value, concerning the load (monthly peak demand, difference between peak and valley demand), environmental (weather) conditions, users and urbanization parameters.…”

Section: Literature Reviewmentioning

confidence: 99%

Estimation of maximum loads of residential electricity users

Bielecki

2019

E3S Web Conf.

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The subject matter of coefficient factors is not a frequent object of analysis in the scientific literature. However, because of the use of this term in engineering tasks and because of the constantly changing manner of energy use (resulting from the dissemination of new appliances), there is a need for current analysis and possible revision of the approach based on coefficient factors. The aim of the paper is to compare currently recommended values of this coefficient in the task of estimating the peak output of installations on the example of standards applied in Poland. On the basis of smart metering data and statistical methods, coefficient factors for residential consumers supplied from the municipal distribution network were determined. The results were compared with the currently applied values in accordance with the industry standard issued by the Association of Polish Electrical Engineers (standard N SEP-E-002).

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Selection method of community load coincidence factor based on BP neural network

Cited by 2 publications

References 8 publications

Reduction of distribution system losses through WAPDA distribution system line- loss reduction program

Reduction of distribution system losses through WAPDA distribution system line- loss reduction program

Estimation of maximum loads of residential electricity users

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