Estimation of a preference map of new consumers for spatial load forecasting simulation methods using a spatial analysis of points

Melo, Joel D.; Carreno, E. M.; Padilha‐Feltrin, Antonio

doi:10.1016/j.ijepes.2014.11.023

Cited by 18 publications

(10 citation statements)

References 14 publications

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“…Another grid cells' states, that is, the grid cell's probabilities were calculated by binary regression through a generalized additive model in order to characterize the preference of the inhabitants by specific locations .…”

Section: Resultsmentioning

confidence: 99%

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Grid-based simulation method for spatial electric load forecasting using power-law distribution with fractal exponent

Melo

Carreno

Padilha‐Feltrin

et al. 2015

Int. Trans. Electr. Energ. Syst.

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SUMMARYA grid-based simulation method to forecast the spatial growth of load density in a distribution utility service zone is presented. The future load density is simulated considering a city's dynamic growth. A power-law distribution with fractal exponent is used to determine how much the load density will increase/decrease depending on different factors like: natural growth following patterns, spontaneous new loads without predefined patterns, and urban poles growth that attract/repel new consumers. These factors are simulated using two modules that exchange information. The final result will display a map with future load density considering a heterogeneous distribution, which is the main contribution of this work. The method is tested on a real distribution system in a medium-sized Brazilian city. The most important characteristic of this method is its high efficiency in spatial load density recognition with less input data than traditional spatial load forecasting methodologies.

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Section: Resultsmentioning

confidence: 99%

“…The computational time to simulate the load density growth is approximately 5 minutes using a computer with an Intel Core 2 Duo Processor 2.99 GHz, 3.5 GB RAM memory, using the software r , version 2.13.2 .…”

Section: Resultsmentioning

confidence: 99%

Grid-based simulation method for spatial electric load forecasting using power-law distribution with fractal exponent

Melo

Carreno

Padilha‐Feltrin

et al. 2015

Int. Trans. Electr. Energ. Syst.

Self Cite

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show abstract

“…A power law distribution with fractal exponent is used to determine the load density, depending on different factors. A spatial analysis of points is proposed in [16], especially suited to estimating a preference map for new consumers, which is then used as an analytical tool in spatial electric load forecasting. However, the methods of the above references have neglected mining and have not utilized historical data.…”

Section: Introductionmentioning

confidence: 99%

A spatial load forecasting method based on load regularity analysis

Bai¹,

Liu²

2017

Turk J Elec Eng & Comp Sci

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Abstract:A new method, based on regularity analysis of cellular load, is presented to determine the reasonable maximum of the load density index method of spatial load forecasting (SLF). Through analysis of cellular historical load data, each cellular load is decomposed into regular and random components. The regular part is used to reveal the regularity of the cellular load in different cycles, using random components to describe and characterize the intrinsic error of the cellular load. Adverse effects that occur by eliminating random components to suppress the intrinsic error allow extraction of the maximum of the regular part as the Class I cellular reasonable load maximum. Using the reasonable maximum and the land use information of Class I cells, load density of I is obtained. The load density is used to determine the coordination coefficient of the classification load density. According to the constraint relationship of the historical load, we established the power supply area, land information, classification load density, relationship equation between Class I cellular load, and classification load density. Classification load density index is obtained by using the least square method and is used to predict its value in the target year. Then the load of Class II cell can be obtained, so that the spatial load forecasting of the urban power system is realized. The analysis of an example indicates that the method is effective in improving the prediction accuracy of spatial load forecasting.

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“…For example, in [14], a spatial point pattern analysis to determine the input data for a spatial-temporal simulation of the load growth is explained and in [15], a spatial-temporal approach for estimating the load demand of battery electric vehicles charging in small residential areas was proposed.…”

Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Estimation for Nontechnical Losses

Faria

Melo

Padilha‐Feltrin

2016

IEEE Trans. Power Delivery

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This paper presents a novel method for estimating the spatial distribution in geographical space of the nontechnical losses over time. The method progresses in two stages: in the first stage, a generalized additive model is used to generate a map of current loss probabilities. The second stage employs the Markov chain to generate a map that indicates possible future changes in loss probabilities. The method yields an assessment of the location of the non-technical losses now and in the future at the city subarea level, even indicating the variables that have greater statistical correlation with the non-technical losses. We apply the method to a city with approximately 81,000 consumers and the results are compared with those obtained through inspections carried out by a Brazilian power utility. The detection rate surpasses 78% in inspected subareas. The method we propose offers improved estimation of distribution of the non-technical losses in urban regions. Index Terms -Electricity theft, generalized additive models, non-technical losses, spatial point pattern analysis.0885-8977 (c) . His main fields of insterest are analysis and control of electrical power systems.

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Estimation of a preference map of new consumers for spatial load forecasting simulation methods using a spatial analysis of points

Cited by 18 publications

References 14 publications

Grid-based simulation method for spatial electric load forecasting using power-law distribution with fractal exponent

Grid-based simulation method for spatial electric load forecasting using power-law distribution with fractal exponent

A spatial load forecasting method based on load regularity analysis

Spatial-Temporal Estimation for Nontechnical Losses

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