day-ahead price forecasting of electricity markets by a new hybrid forecast method
نویسندگان
چکیده
energy price forecast is the key information for generating companies to prepare their bids in the electricity markets. however, this forecasting problem is complex due to nonlinear, non-stationary, and time variant behavior of electricity price time series. accordingly, in this paper a new strategy is proposed for electricity price forecast. the forecast strategy includes wavelet transform (wt), auto-regressive integrated moving average (arima) and radial basis function neural networks (rbfn). also, an intelligent algorithm is applied to optimize the rbfn structure, which adapts it to the specified training set, reduce computational complexity and avoids overfitting. in the proposed forecast strategy, the wt provides a set of better-behaved constitutive series, arima generates a linear forecast and rbfn is developed as a tool for nonlinear pattern recognition to correct the forecast error. the proposed strategy is applied for price forecasting of electricity market of mainland spain and its results are compared with the results of several other price forecast methods. these comparisons confirm the validity of the developed approach.
منابع مشابه
Day-ahead Price Forecasting of Electricity Markets by a New Hybrid Forecast Method
Energy price forecast is the key information for generating companies to prepare their bids in the electricity markets. However, this forecasting problem is complex due to nonlinear, non-stationary, and time variant behavior of electricity price time series. Accordingly, in this paper a new strategy is proposed for electricity price forecast. The forecast strategy includes Wavelet Transform (WT...
متن کاملApplication of a New Hybrid Method for Day-Ahead Energy Price Forecasting in Iranian Electricity Market
Abstract- In a typical competitive electricity market, a large number of short-term and long-term contracts are set on basis of energy price by an Independent System Operator (ISO). Under such circumstances, accurate electricity price forecasting can play a significant role in improving the more reasonable bidding strategies adopted by the electricity market participants. So, they cannot only r...
متن کاملA Hybrid Artificial Neural Network and VEPSO based on Day-ahead Price Forecasting of Electricity Markets
In this paper a new Hybrid technique of Artificial Neural Network (ANN) and Vector Evaluated Particle Swarm Optimization (VEPSO) is presented as a forecasting strategy for day-ahead price of electricity market. The proposed technique the proposed intelligent technique is applied to weights and bias of ANN to improve the learning capability through the minimum error. A comprehensive comparative ...
متن کاملDay-Ahead Electricity Price Forecasting Using a Hybrid Principal Component Analysis Network
Bidding competition is one of the main transaction approaches in a deregulated electricity market. Locational marginal prices (LMPs) resulting from bidding competition and system operation conditions indicate electricity values at a node or in an area. The LMP reveals important information for market participants in developing their bidding strategies. Moreover, LMP is also a vital indicator fo...
متن کاملA Hybrid Multi-Step Model for Forecasting Day-Ahead Electricity Price Based on Optimization, Fuzzy Logic and Model Selection
The day-ahead electricity market is closely related to other commodity markets such as the fuel and emission markets and is increasingly playing a significant role in human life. Thus, in the electricity markets, accurate electricity price forecasting plays significant role for power producers and consumers. Although many studies developing and proposing highly accurate forecasting models exist...
متن کاملمنابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
modeling and simulation in electrical and electronics engineeringناشر: semnan university
ISSN
دوره 1
شماره 1 2015
کلمات کلیدی
میزبانی شده توسط پلتفرم ابری doprax.com
copyright © 2015-2023