Short-term electricity price forecasting using wavelet and SVM techniques

In deregulated electricity market, modeling and forecasting the spot price present a number of challenges. By applying wavelet and support vector machine techniques, a new time series model for short term electricity price forecasting has been developed in this paper. The model employs both historical price and other important information, such as load capacity and weather (temperature), to forecast the price of one or more time steps ahead. The developed model has been evaluated with the actual data from Australian National Electricity Market. The simulation results demonstrated that the forecast model is capable of forecasting the electricity price with a reasonable forecasting accuracy.

Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models

This paper presents some forecasting techniques for energy demand and price prediction, one day ahead. These techniques combine wavelet transform (WT) with fixed and adaptive machine learning/time series models (multi-layer perceptron (MLP), radial basis functions, linear regression, or GARCH). To create an adaptive model, we use an extended Kalman filter or particle filter to update the parameters continuously on the test set. The adaptive GARCH model is a new contribution, broadening the applicability of GARCH methods. We empirically compared two approaches of combining the WT with prediction models: multicomponent forecasts and direct forecasts. These techniques are applied to large sets of real data (both stationary and non-stationary) from the UK energy markets, so as to provide comparative results that are statistically stronger than those previously reported. The results showed that the forecasting accuracy is significantly improved by using the WT and adaptive models. The best models on the electricity demand/gas price forecast are the adaptive MLP/GARCH with the multicomponent forecast; their MSEs are 0.02314 and 0.15384 respectively.

Creating innovation in small and medium-sized enterprises:evaluating the short-term effects of the creative credits pilot

This working paper looks at the short-term impact of the Creative Credits pilot scheme which operated in the Manchester City Region in the North West of England from September 2009 to September 2010, and was funded by NESTA, Manchester City Council, the North West Development Agency, the Economic and Social Research Council (ESRC) and the Arts and Humanities Research Council (AHRC). Creative Credits is a business-to-business (B2B) voucher mechanism designed to encourage small and medium-sized enterprises (SMEs) to work innovatively with creative companies. Businesses receive credits worth £4,000, which they must match with at least £1,000, to spend with creative firms on a variety of creative services.