An expert system based algorithm for short term load forecast

Rahman, Saifur; Bhatnagar, R.

doi:10.1109/59.192889

Cited by 397 publications

(90 citation statements)

References 13 publications

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“…However, the electric power load forecasting problem is not easy to handle due to its nonlinear and random-like behaviors of system loads, weather conditions, and variations of social and economic environments, etc. Many studies have been reported to improve the accuracy of load forecasting using the conventional methods such as regression-based method [14], Kalman filter [15], and knowledge-based expert system [16]. However, these techniques have a possibility to lack the accuracy of prediction with the higher load forecasting errors in some particular time zones, which are, for example, the weekdays of the summer season, weekend, and/or Monday.…”

Section: Hybrid Algorithmmentioning

confidence: 99%

“…However, these techniques have a possibility to lack the accuracy of prediction with the higher load forecasting errors in some particular time zones, which are, for example, the weekdays of the summer season, weekend, and/or Monday. To overcome this problem, the computational intelligence techniques [16]- [24], which are the fuzzy systems and artificial neural networks [19,22,27], have been investigated in the past decade as an alternative to the conventional methods.…”

Section: Hybrid Algorithmmentioning

confidence: 99%

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Artificial Neural Network Based Hybrid Algorithmic Structure for Solving Linear Programming Problems

Babu¹,

Palaniappan²

2010

IJCEE

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Section: Hybrid Algorithmmentioning

confidence: 99%

Section: Hybrid Algorithmmentioning

confidence: 99%

Artificial Neural Network Based Hybrid Algorithmic Structure for Solving Linear Programming Problems

Babu¹,

Palaniappan²

2010

IJCEE

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“…Recently, an expert system has been used to predict the load after establishing a database using the knowledge and experience of experts [4,5]. Another method for predicting the short-term load involves the use of a nerve circuit network, which is an intelligent system [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Short-term Electric Load Forecasting Using Data Mining Technique

Kim¹,

Koo²,

Park³

2012

Journal of Electrical Engineering and Technology

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-In this paper, we introduce data mining techniques for short-term load forecasting (STLF).First, we use the K-mean algorithm to classify historical load data by season into four patterns. Second, we use the k-NN algorithm to divide the classified data into four patterns for Mondays, other weekdays, Saturdays, and Sundays. The classified data are used to develop a time series forecasting model. We then forecast the hourly load on weekdays and weekends, excluding special holidays. The historical load data are used as inputs for load forecasting. We compare our results with the KEPCO hourly record for 2008 and conclude that our approach is effective.

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“…The characteristic feature of this approach is that it is rule-based, which implies that the system transforms new rules from received information. In other words, it is presumed that an expert trained using existing data will provide increased forecasting accuracy [14][15][16]. This approach is a derivation of the rules from on-the-job training and sometimes transforming the information logic into equations could be impractical.…”

mentioning

confidence: 99%

“…Knowledge-based expert systems (KBES') and artificial neural networks (ANNs) are the popular representatives. The KBES approaches performed electric load forecasting by simulating the experiences of system operators who were well-experienced in the electricity generation processes, such as Rahman and Bhatnagar [14]. The characteristic feature of this approach is that it is rule-based, which implies that the system transforms new rules from received information.…”

mentioning

confidence: 99%

SVR with Hybrid Chaotic Immune Algorithm for Seasonal Load Demand Forecasting

et al. 2011

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Accurate electric load forecasting has become the most important issue in energy management; however, electric load demonstrates a seasonal/cyclic tendency from economic activities or the cyclic nature of climate. The applications of the support vector regression (SVR) model to deal with seasonal/cyclic electric load forecasting have not been widely explored. The purpose of this paper is to present a SVR model which combines the seasonal adjustment mechanism and a chaotic immune algorithm (namely SSVRCIA) to forecast monthly electric loads. Based on the operation procedure of the immune algorithm (IA), if the population diversity of an initial population cannot be maintained under selective pressure, then IA could only seek for the solutions in the narrow space and the solution is far from the global optimum (premature convergence). The proposed chaotic immune algorithm (CIA) based on the chaos optimization algorithm and IA, which diversifies the initial definition domain in stochastic optimization procedures, is used to overcome the premature local optimum issue in determining three parameters of a SVR model. A numerical example from an existing reference is used to elucidate the forecasting performance of the proposed SSVRCIA model. The forecasting results indicate that OPEN ACCESSEnergies 2011, 4 961 the proposed model yields more accurate forecasting results than the ARIMA and TF-ε-SVR-SA models, and therefore the SSVRCIA model is a promising alternative for electric load forecasting.

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An expert system based algorithm for short term load forecast

Cited by 397 publications

References 13 publications

Artificial Neural Network Based Hybrid Algorithmic Structure for Solving Linear Programming Problems

Artificial Neural Network Based Hybrid Algorithmic Structure for Solving Linear Programming Problems

Short-term Electric Load Forecasting Using Data Mining Technique

SVR with Hybrid Chaotic Immune Algorithm for Seasonal Load Demand Forecasting

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