Determination of customer load characteristics by load survey system at Taipower

Chen, C.S.; Hwang, Jau-Chung; Tzeng, Y.M.; Huang, C.W.; Cho, M.Y.

doi:10.1109/61.517501

Cited by 117 publications

(36 citation statements)

References 10 publications

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“…The advantage is the need to forecast demographic and economic factors. Typically, the factors such as population, building permits, business, weather data and the like are used in correlation techniques [4][5][6][7][8].…”

Section: Proposed Modelmentioning

confidence: 99%

A Hybrid Short Term Load Forecasting Model of an Indian Grid

Behera¹,

Panigrahi²,

Pati³

2011

EPE

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This paper describes an application of combined model of extrapolation and correlation techniques for short term load forecasting of an Indian substation. Here effort has been given to improvise the accuracy of electrical load forecasting considering the factors, past data of the load, respective weather condition and financial growth of the people. These factors are derived by curve fitting technique. Then simulation has been conducted using MATLAB tools. Here it has been suggested that consideration of 20 years data for a developing country should be ignored as the development of a country is highly unpredictable. However, the importance of the past data should not be ignored. Here, just previous five years data are used to determine the above factors.

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Section: Proposed Modelmentioning

confidence: 99%

A Hybrid Short Term Load Forecasting Model of an Indian Grid

Behera¹,

Panigrahi²,

Pati³

2011

EPE

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“…Apart from consumption data, load surveys seek to gather weather data, consumer preferences, occupancy behaviour and others [23,24]. The accuracy of load surveys depends on the characteristics of the eligible sample of consumers [25,26]. Following a bottom-up-approach, the findings of load surveys on the eligible set are scaled up to include the rest consumers.…”

Section: Literature Survey and Contributionsmentioning

confidence: 99%

Optimal Selection of Clustering Algorithm via Multi-Criteria Decision Analysis (MCDA) for Load Profiling Applications

Panapakidis

Christoforidis

2018

Applied Sciences

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Due to high implementation rates of smart meter systems, considerable amount of research is placed in machine learning tools for data handling and information retrieval. A key tool in load data processing is clustering. In recent years, a number of researches have proposed different clustering algorithms in the load profiling field. The present paper provides a methodology for addressing the aforementioned problem through Multi-Criteria Decision Analysis (MCDA) and namely, using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). A comparison of the algorithms is employed. Next, a single test case on the selection of an algorithm is examined. User specific weights are applied and based on these weight values, the optimal algorithm is drawn.

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“…As example, we can treat all profiles belonging to a class and obtain a statistical distribution that explain customers consumption by means of average and deviation profiles [5], [6], [7]. In other cases, statistics are presented as tools used in the own classification process.…”

Section: A Clustering Algorithmsmentioning

confidence: 99%

“…Using the best map obtained in the previous simulations, the Hourly Load Profile, the 20 customers simulated can be classified in five different zones: University -1, 2, 9, 10, 11, 12, 13, 14, 15 and 16-, 6,7,8,19 and 20-, Medium Industry -3, 4-and Small Industry -17 and 18-(see figure 8). …”

Section: A Customer Segmentation Criteriamentioning

confidence: 99%

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Characterization and identification of electrical customers through the use of self-organizing maps and daily load parameters

Verdú

García

Franco³

et al.

IEEE PES Power Systems Conference and Exposition, 2004.

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Abstract--This paper shows the capacity of modern computational techniques such as the self-organizing map (SOM) as a methodology to achieve the classification of the electrical customers in a commercial or geographical area. This approach allows to extract the pattern of customer behavior from historic load demand series. Several ways of data analysis from load curves can be used to get different input data to "feed" the neural network. In this work, we propose two methods to improve customer clustering: the use of frequency-based indices and the use of the hourly load curve. Results of a case study developed on a set of different spanish customers and a comparison between the two approachs proposed here are presented.

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Determination of customer load characteristics by load survey system at Taipower

Cited by 117 publications

References 10 publications

A Hybrid Short Term Load Forecasting Model of an Indian Grid

A Hybrid Short Term Load Forecasting Model of an Indian Grid

Optimal Selection of Clustering Algorithm via Multi-Criteria Decision Analysis (MCDA) for Load Profiling Applications

Characterization and identification of electrical customers through the use of self-organizing maps and daily load parameters

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