Electricity demand and supply forecasts assume special significance vis-Ii-vis ever-increasing demand. Different agencies and authors, to forecast demand, have used various methodologies but on the other hand, no concerted effort is visible in predicting the supply. Moreover none of the methodologies used can be considered perfect. The present study attempts to forecast the two and tends to rea...

Gold price has significant nonlinearity and time-variance with many indeterminate influencing factors. In order to improve the forecast accuracy of gold price, this paper puts forward a gold price forecast model combing projection pursuit with neural network. At first, projection pursuit algorithm is used to screen the influencing factors, and then the influencing factors are used as the input ...

The incentive on an electricity generating firm to exercise market power depends strongly on the volume the firm has pre-sold in the forward or hedge markets. Therefore, in order to forecast the effect of mergers and other market developments on market power outcomes, it is essential to model the hedging decisions of dominant generating firms. This paper shows that a dominant firm’s profit-maxi...

Modelling and forecasting the volatile spot pricing process for electricity presents a number of challenges. For increasingly deregulated electricity markets, like that in the Australian state of New South Wales, there is a need to forecast the price for a range of derivative securities used for hedging. Any forecasting model that hopes to capture the pricing dynamics within this market must be...

Interest in compressed air energy storage (CAES) technology has been renewed driven by the need to manage variability form rapidly growing wind and solar capacity. Distributed CAES (D-CAES) design aims to improve the efficiency of conventional CAES through locating the compressor near concentrated heating loads so capturing additional revenue through sales of compression waste heat. A pipeline ...

In recent years, coal price has risen rapidly, which has also brought a sharp increase in the expenditures of thermal power plants in China. Meantime, the power production price and power retail price have not been adjusted accordingly and a large number of thermal power plants have incurred losses. The power industry is a key industry in the national economy. As such, a thorough analysis and e...

In this job, short-term forecasts are calculated for the Energy Price in the Electricity Production Market of Spain. The methodology used to achieve these forecasts is based on Artificial Neural Networks, which have been used succesfully in recent years in many forecasting applications. To gauge the quality of forecasts, they have been compared with those obtained with the Box-Jenkins ARIMA mod...

Sensors distributed all around electrical-power distribution networks produce streams of data at high-speed. From a data mining perspective, this sensor network problem is characterized by a large number of variables (sensors), producing a continuous flow of data, in a dynamic non-stationary environment. Companies make decisions to buy or sell energy based on load profiles and forecast. We prop...

The market clearing prices in deregulated electricity markets are volatile. Good market clearing price forecasting will help producers and consumers to prepare their corresponding bidding strategies so as to maximize their profits. Market clearing price prediction is a difficult task since bidding strategies used by market participants are complicated and various uncertainties interact in an in...