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.

Day-ahead ,melectricity price forecasting using the wavelet transform and arima models

Electricity price forecasting in deregulated markets: A review and evaluation

https://ubibliorum.ubi.pt/bitstream/10400.6/645/1/ESPR2_JPSCatalao.pdf

Short-term electricity prices forecasting in a competitive market: A neural network approach

http://prac.im.pwr.edu.pl/~hugo/publ/RWeronMisiorek08_IJF.pdf

Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models

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.

Short-Term Load Forecasting: Similar Day-Based Wavelet Neural Networks

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.

Kasiviswanathan

Papers

Energy Clearing Price Prediction and Confidence Interval Estimation with Cascaded Neural Networks