آمار

تعداد نشریات30
تعداد شماره‌ها426
تعداد مقالات3,744
تعداد مشاهده مقاله5,876,042
تعداد دریافت فایل اصل مقاله4,066,017
Taherian, H., Nazer, I., Razavi, E., Goldani, S. R., Farshad, M., Aghaebrahimi, M. R.. (1386). Application of an Improved Neural Network Using Cuckoo Search Algorithm in Short-Term Electricity Price Forecasting under Competitive Power Markets. سامانه مدیریت نشریات علمی, 1(2), 136-146.
H. Taherian; I. Nazer; E. Razavi; S. R. Goldani; M. Farshad; M. R. Aghaebrahimi. "Application of an Improved Neural Network Using Cuckoo Search Algorithm in Short-Term Electricity Price Forecasting under Competitive Power Markets". سامانه مدیریت نشریات علمی, 1, 2, 1386, 136-146.
Taherian, H., Nazer, I., Razavi, E., Goldani, S. R., Farshad, M., Aghaebrahimi, M. R.. (1386). 'Application of an Improved Neural Network Using Cuckoo Search Algorithm in Short-Term Electricity Price Forecasting under Competitive Power Markets', سامانه مدیریت نشریات علمی, 1(2), pp. 136-146.
Taherian, H., Nazer, I., Razavi, E., Goldani, S. R., Farshad, M., Aghaebrahimi, M. R.. Application of an Improved Neural Network Using Cuckoo Search Algorithm in Short-Term Electricity Price Forecasting under Competitive Power Markets. سامانه مدیریت نشریات علمی, 1386; 1(2): 136-146.

Application of an Improved Neural Network Using Cuckoo Search Algorithm in Short-Term Electricity Price Forecasting under Competitive Power Markets

Journal of Operation and Automation in Power Engineering
مقاله 6، دوره 1، شماره 2، بهمن 2013، صفحه 136-146    اصل مقاله (181.21 K)
نوع مقاله: Research paper
نویسندگان
H. Taherian* ؛ I. Nazer؛ E. Razavi؛ S. R. Goldani؛ M. Farshad؛ M. R. Aghaebrahimi
Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran.
چکیده
Accurate and effective electricity price forecasting is critical to market participants in order to make an appropriate risk management in competitive electricity markets. Market participants rely on price forecasts to decide on their bidding strategies, allocate assets and plan facility investments. However, due to its time variant behavior and non-linear and non-stationary nature, electricity price is a complex signal. This paper presents a model for short-term price forecasting according to similar days and historical price data. The main idea of this article is to present an intelligent model to forecast market clearing price using a multilayer perceptron neural network, based on structural and weights optimization. Compared to conventional neural networks, this hybrid model has high accuracy and is capable of converging to optimal minimum. The results of this forecasting method for Market Clearing Price (MCP) of Iranian and Nord Pool Electricity Markets, as well as Locational Marginal Price (LMP) forecasting in PJM electricity market, verify the effectiveness of the proposed approach in short-term price forecasting.
کلیدواژه‌ها
Short-term Price Forecasting؛ Artificial Neural Network؛ CUCKOO Search Algorithm؛ genetic algorithm؛ Similar Days
آمار
تعداد مشاهده مقاله: 2,831
تعداد دریافت فایل اصل مقاله: 2,576


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب