Midterm Demand Prediction of Electrical Power Systems Using a New Hybrid Forecast Technique

Amjady, Nima; Daraeepour, Ali

doi:10.1109/tpwrs.2010.2055902

Cited by 58 publications

(23 citation statements)

References 26 publications

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“…Overfitting frequently occurs in the load forecast problems, and many research works in this area have discussed about it, such as [20][21][22]. In this case, the NN fits well the training samples leading to a low training error, but does not learn the input/output mapping function of training samples, and so its prediction error for unseen forecast samples is unreasonably high.…”

Section: Application Of the Proposed θ-Sso For Optimizing The Rbfnn Smentioning

confidence: 99%

Short-term load forecast of electrical power system by radial basis function neural network and new stochastic search algorithm

Abedinia

Amjady

2015

Int. Trans. Electr. Energ. Syst.

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Summary In this paper a new model of radial basis function (RBF) neural network based on a novel stochastic search algorithm is presented for short‐term load forecast (STLF). STLF is an important operation function in both regulated and deregulated power systems. Accurate STLF is effective for area generation control and resource dispatch as well as electricity market clearing. The proposed STLF method optimizes the structure of the RBF‐based forecasting engine. Random selection of the number of hidden neurons may cause overfitting or underfitting problem of the network. For this purpose, a new stochastic search algorithm is presented to find the optimum number of neurons for the hidden layer. To demonstrate the effectiveness of the proposed STLF approach, it is tested on three real‐world engineering case studies, and the obtained results are compared with the results of several other recently published STLF methods. These comparisons confirm the validity of the developed approach. Copyright © 2015 John Wiley & Sons, Ltd.

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Section: Application Of the Proposed θ-Sso For Optimizing The Rbfnn Smentioning

confidence: 99%

Short-term load forecast of electrical power system by radial basis function neural network and new stochastic search algorithm

Abedinia

Amjady

2015

Int. Trans. Electr. Energ. Syst.

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“…Energy forecasting methods are classified based on different aspects. From the time duration point of view, energy forecasting is divided into four categories: long‐term, midterm, short‐term, and very short‐term . This paper focuses on long‐term energy forecasting.…”

Section: Proposed Methods For Energy Forecastingmentioning

confidence: 99%

A new combined method for future energy forecasting in electrical networks

Baesmat

Shiri

2018

Int Trans Electr Energ Syst

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Summary Growing the industry and population in a region leads to the growth of required amount of electrical energy. Electrical companies should provide high quality energy according to the demand of customers. Load is an effective parameter of any system, and the network programmers should consider the annual load growth of the system and predict the required energy for each region by investigating previously recorded data of consumed electrical energy and stochastic analysis. In this paper, a new combined method for long‐term energy forecasting is proposed. This method, which combines the land‐consumption method and curve fitting based on generalization method, in addition to having simple calculations, takes into account the saturation. Moreover, loads from detailed formal program provided by the relevant institutes have been used, which results in better coordination between all organizations in charge of energy predictions and development of the countries. A suitable filtering method is also employed for input data to improve the method accuracy. To show the effectiveness of the proposed method, the results of different methods have been compared with those of the proposed method as well as real data. Then, real data of former 11 years of consumed energy gathered from Shiraz Electrical Distribution Company subscribers are employed and the energy for future 11 years is forecasted.

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“…Trending methods, on the other hand, are based on the assumption that the process of growth and changes will be observed in the historical data remains constant in the future [4]. Two issues may invalidate the use of trending methods in TAFILA: (i) changes in growth factors, and (ii) the needs of multiscenario planning because of the ambiguous development of large projects.…”

Section: The Spatial Load Forecasting Model and Methods Choosingmentioning

confidence: 99%

Spatial Load Forecast and estimation of the peak electricity demand location for Tafila region

Saraereh

Alsafasfeh

Wang

2016

2016 7th International Renewable Energy Congress (IREC)

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The aim of this paper is to provide a forecast of the peak demand for the next 15 years for electrical distribution companies in Tafila region of Jordan. The proposed methodology provides both the peak demand and its location for the next 15 years. This paper describes the Spatial Load Forecasting model used, the information provided by electrical distribution company in Jordan, the workflow followed, the parameters used and the assumptions made to run the model.

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Midterm Demand Prediction of Electrical Power Systems Using a New Hybrid Forecast Technique

Cited by 58 publications

References 26 publications

Short-term load forecast of electrical power system by radial basis function neural network and new stochastic search algorithm

Short-term load forecast of electrical power system by radial basis function neural network and new stochastic search algorithm

A new combined method for future energy forecasting in electrical networks

Spatial Load Forecast and estimation of the peak electricity demand location for Tafila region

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