Author
Luh
Bio: Luh is an academic researcher. The author has contributed to research in topics: Market clearing & Bayesian inference. The author has an hindex of 1, co-authored 1 publications receiving 107 citations.
Papers
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TL;DR: This paper analyzes the uncertainties involved in a cascaded neural network structure for MCP prediction and develops the prediction distribution under the Bayesian framework, which is computationally efficient and provides accurate prediction and confidence coverage.
Abstract: The deregulated power market is an auction market, and energy market clearing prices (MCP) are volatile. Good MCP prediction and its confidence interval estimation will help utilities and independent power producers submit effective bids with low risks. MCP prediction, however, is difficult since bidding strategies used by participants are complicated and various uncertainties interact in an intricate way. Furthermore, MCP predictors usually have a cascaded structure, as several key input factors need to be estimated first. Cascaded structures are widely used, however, they have not been adequately investigated. This paper analyzes the uncertainties involved in a cascaded neural network structure for MCP prediction and develops the prediction distribution under the Bayesian framework. A fast algorithm to evaluate the confidence intervals by using the memoryless quasi-Newton method is also developed. Testing results on a classroom problem and on New England MCP prediction show that the method is computationally efficient and provides accurate prediction and confidence coverage. The scheme is generic, and can be applied to various networks, such as multilayer perceptrons and radial basis function networks.
107 citations
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01 Jan 2005
TL;DR: In this paper, the authors proposed a novel technique to forecast day-ahead electricity prices based on the wavelet transform and ARIMA models, in which the historical and usually ill-behaved price series is decomposed using the Wavelet transform in a set of better-behaving constitutive series.
Abstract: This paper proposes a novel technique to forecast day-ahead electricity prices based on the wavelet transform and ARIMA models. The historical and usually ill-behaved price series is decomposed using the wavelet transform in a set of better-behaved constitutive series. Then, the future values of these constitutive series are forecast using properly fitted ARIMA models. In turn, the ARIMA forecasts allow, through the inverse wavelet transform, reconstructing the future behavior of the price series and therefore to forecast prices. Results from the electricity market of mainland Spain in year 2002 are reported.
767 citations
TL;DR: The main methodologies used in electricity price forecasting have been reviewed in this paper and classification of various price-influencing factors used by different researchers has been done and put for reference.
492 citations
TL;DR: In this article, a neural network approach for forecasting short-term electricity prices is proposed. But the authors focus on the short term and do not consider the long-term forecast of electricity prices, and use a three-layered feed-forward neural network for forecasting next-week electricity prices.
402 citations
TL;DR: In this article, the authors compared the performance of 12 time series methods for short-term (day-ahead) spot price forecasting in auction-type electricity markets, including spike preprocessed, threshold and semiparametric autoregressions, as well as mean-reverting jump diffusions.
378 citations
TL;DR: In this paper, a similar day-based wavelet neural network method was used to forecast tomorrow's load in deregulated electricity markets, which is important for reliable power system operation and also significantly affects markets and their participants.
Abstract: In deregulated electricity markets, short-term load forecasting is important for reliable power system operation, and also significantly affects markets and their participants. Effective forecasting, however, is difficult in view of the complicated effects on load by a variety of factors. This paper presents a similar day-based wavelet neural network method to forecast tomorrow's load. The idea is to select similar day load as the input load based on correlation analysis, and use wavelet decomposition and separate neural networks to capture the features of load at low and high frequencies. Despite of its "noisy" nature, high frequency load is well predicted by including precipitation and high frequency component of similar day load as inputs. Numerical testing shows that this method provides accurate predictions.
375 citations