Journal ArticleDOI
A Statistical Approach for Interval Forecasting of the Electricity Price
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TLDR
In this article, a data mining-based approach is proposed to forecast the value of the electricity price series, which is widely accepted as a nonlinear time series, and to accurately estimate the prediction interval of the electric price series.Abstract:
Electricity price forecasting is a difficult yet essential task for market participants in a deregulated electricity market. Rather than forecasting the value, market participants are sometimes more interested in forecasting the prediction interval of the electricity price. Forecasting the prediction interval is essential for estimating the uncertainty involved in the price and thus is highly useful for making generation bidding strategies and investment decisions. In this paper, a novel data mining-based approach is proposed to achieve two major objectives: 1) to accurately forecast the value of the electricity price series, which is widely accepted as a nonlinear time series; 2) to accurately estimate the prediction interval of the electricity price series. In the proposed approach, support vector machine (SVM) is employed to forecast the value of the price. To forecast the prediction interval, we construct a statistical model by introducing a heteroscedastic variance equation for the SVM. Maximum likelihood estimation (MLE) is used to estimate model parameters. Results from the case studies on real-world price data prove that the proposed method is highly effective compared with existing methods such as GARCH models.read more
Citations
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Journal ArticleDOI
Electricity price forecasting: A review of the state-of-the-art with a look into the future
TL;DR: In this paper, a review article aims to explain the complexity of available solutions, their strengths and weaknesses, and the opportunities and threats that the forecasting tools offer or that may be encountered.
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Electricity price forecasting: A review of the state-of-the-art with a look into the future
TL;DR: In this article, a review article aims at explaining the complexity of available solutions, their strengths and weaknesses, and the opportunities and treats that the forecasting tools offer or that may be encountered.
Journal ArticleDOI
Real-Time Price-Based Demand Response Management for Residential Appliances via Stochastic Optimization and Robust Optimization
TL;DR: The numerical results show attributes of the two approaches for solving the real-time optimal DR management problem for residential appliances via stochastic optimization and robust optimization approaches.
Journal ArticleDOI
Lower Upper Bound Estimation Method for Construction of Neural Network-Based Prediction Intervals
TL;DR: A new, fast, yet reliable method for the construction of PIs for NN predictions, and the quantitative comparison with three traditional techniques for prediction interval construction reveals that the LUBE method is simpler, faster, and more reliable.
Journal ArticleDOI
Comprehensive Review of Neural Network-Based Prediction Intervals and New Advances
TL;DR: The quality of PIs produced by the combiners is dramatically better than the quality ofPIs obtained from each individual method and a new method for generating combined PIs using the traditional PIs is proposed.
References
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Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Journal ArticleDOI
Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation
TL;DR: In this article, a new class of stochastic processes called autoregressive conditional heteroscedastic (ARCH) processes are introduced, which are mean zero, serially uncorrelated processes with nonconstant variances conditional on the past, but constant unconditional variances.
Journal ArticleDOI
Generalized autoregressive conditional heteroskedasticity
Tim Bollerslev,Tim Bollerslev +1 more
TL;DR: In this paper, a natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in 1982 to allow for past conditional variances in the current conditional variance equation is proposed.
Journal ArticleDOI
A Tutorial on Support Vector Machines for Pattern Recognition
TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.