Object-Oriented Genetic Algorithm Based Artificial Neural Network for Load Forecasting

Lai, Loi Lei; Subasinghe, H.; Rajkumar, N.; Vaseekar, E.; Gwyn, B.; Sood, Vijay K.

doi:10.1007/3-540-48873-1_59

Cited by 8 publications

(6 citation statements)

References 7 publications

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“…In this paper, the network composes of 51 inputs and 24 outputs and it is simulated by MATLAB . [21] presents short-term load forecasting by combining neural network and genetic algorithm with the case study in Taiwan while [22] presents the implementation of genetic algorithm method for fastening computation and increasing forecasting accuracy. The time period of this load forecast value is in 24 hours.…”

Section: Load Forecasting Classification and Paper Reviewedmentioning

confidence: 99%

“…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%

See 1 more Smart Citation

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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Section: Load Forecasting Classification and Paper Reviewedmentioning

confidence: 99%

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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“…Often the performance of short term load forecasters as reported in the literature is evaluated using actual temperatures. However, when the short term load forecasting is actually used at a utility, these future temperatures are not known and forecasts must be used instead [42,43].…”

Section: Introductionmentioning

confidence: 99%

Intelligent energy management

Raghavan

Khaligh

2012

Proceedings of the Workshop on Performance Metrics for Intelligent Systems

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“…Today the main computational intelligence techniques found in power system applications are artificial neural networks (ANNs) [1][2][3][4], evolutionary computing [5][6][7][8][9][10][11][12], fuzzy systems [13][14] and expert systems [15] . Today the main computational intelligence techniques found in power system applications are artificial neural networks (ANNs) [1][2][3][4], evolutionary computing [5][6][7][8][9][10][11][12], fuzzy systems [13][14] and expert systems [15] .…”

Section: Introductionmentioning

confidence: 99%

“…Today the main computational intelligence techniques found in power system applications are artificial neural networks (ANNs) [1-4], evolutionary computing [5][6][7][8][9][10][11][12], fuzzy systems [13][14] and expert systems [15] . It is now necessary for the power producers, transmission network controllers, distribution companies and service providers to cooperate within a competitive framework to supply power cheaply, securely and of a suitable quality.…”

mentioning

confidence: 99%