Mid Term Load Forecasting of the Country Using Statistical Methodology: Case Study in Thailand

Bunnoon, Pituk; Chalermyanont, Kusumal; Limsakul, Chusak

doi:10.1109/icsps.2009.174

Cited by 26 publications

(18 citation statements)

References 3 publications

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“…Therefore the differences brought by time, day type and month should also be included. Overall the parameters should at least encompass the influence of day type, time, month and temperature [11]. These affecting factors are expressed by the variables defined by a kind of 3-layer class in this paper.…”

Section: Mlr Modelmentioning

confidence: 99%

An Innovative Application Method of Monthly Load Forecasting for Smart IEDs

Choi¹,

Liang²,

Lee³

et al. 2013

Journal of Electrical Engineering and Technology

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-This paper develops a new Intelligent Electronic Device (IED), and then presents an application method of a monthly load forecasting algorithm on the smart IEDs. A Multiple Linear Regression (MLR) model implemented with Recursive Least Square (RLS) estimation is established in the algorithm. Case Study proves the accuracy and reliability of this algorithm and demonstrates the practical meanings through designed screens. The application method shows the general way to make use of IED's smart characteristics and thereby reveals a broad prospect of smart function realization in application.

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Section: Mlr Modelmentioning

confidence: 99%

An Innovative Application Method of Monthly Load Forecasting for Smart IEDs

Choi¹,

Liang²,

Lee³

et al. 2013

Journal of Electrical Engineering and Technology

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“…Fig.1 shows the relationship between energy consumption demand and time series [9]. from January 1997 to December 2007 [9] We consider the period from 1997 to 2007 to establish the parameters in forecast model. The original signal (behavior) of energy consumption demand can be seen in Fig.1.…”

Section: A Energy Consumption Demandmentioning

confidence: 99%

“…It depends on a number of complex factors such as seasonal weather, and national economic growth [9].…”

Section: Consumption Demand and Variablesmentioning

confidence: 99%

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Mid-Term Load Forecasting Based on Neural Network Algorithm: a Comparison of Models

Bunnoon¹

2011

IJCEE

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Abstract-This article purposes the model of mid-term energy consumption load forecasting (MTLF) by using artificial neural network based on the two and the three years ahead. This load demand forecasting is a useful tool for a unit commitment and a fuel reserve planning in the power system. Both two and three years ahead forecasting uses two patterns for comparing the accuracy in this research. The results show the two years ahead of load forecasting, model no.2 can be reduced error which Mean Absolute Percentage Error (MAPE) is 4.35%. In three years ahead, the load forecasting model no.1 and no.2, MAPE are almost equal which are equal 4.65% and 4.70%, respectively. Finally, the experimental results show two and three years ahead for the load forecasting by using differential models and a varies number of a neuron in the hidden layer for finding the minimum MAPE of each model.

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“…Algorithm [10], [11], [12], [13], [14], [15], [16], [23], [ 60] Artificial Neural Network [17], [18], [19], [20] Artificial Neural Network + Fuzzy logic [21], [22] Artificial Neural Network + Genetic [24] Fuzzy logic [25], [59] ARIMA+ Artificial Neural Network [26] Regression+ Artificial Neural Network [27], [28], [29] ANN + GAs + Fuzzy [30], [33] Fuzzy logic +Regression [31], [32] Hybrid [55] Support Vector Machine (SVM) *ARIMA = Autoregressive Integrated Moving Average [25] …”

Section: Referencementioning

confidence: 99%

A Computing Model of Artificial Intelligent Approaches to Mid-term Load Forecasting: a state-of-the-art- survey for the researcher

Bunnoon¹,

Chalermyanont²,

Limsakul³

2010

IJET

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Abstract-This article presents the review of the computing models applied for solving problems of midterm load forecasting. The load forecasting results can be used in electricity generation such as energy reservation and maintenance scheduling. Principle, strategy and results of short term, midterm, and long term load forecasting using statistic methods and artificial intelligence technology (AI) are summaried, Which, comparison between each method and the articles have difference feature input and strategy. The last, will get the idea or literature review conclusion to solve the problem of mid term load forecasting (MTLF).

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Mid Term Load Forecasting of the Country Using Statistical Methodology: Case Study in Thailand

Cited by 26 publications

References 3 publications

An Innovative Application Method of Monthly Load Forecasting for Smart IEDs

An Innovative Application Method of Monthly Load Forecasting for Smart IEDs

Mid-Term Load Forecasting Based on Neural Network Algorithm: a Comparison of Models

A Computing Model of Artificial Intelligent Approaches to Mid-term Load Forecasting: a state-of-the-art- survey for the researcher

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