Financing and developing energy projects in a free-market environment requires insight of the future wholesale energy prices. The wide variety of price-driving factors increases the difficulty in predicting the market state in the years to come.
In this scope, iWind has developed a tool for price forecasting using Neural Networks algorithms. It uses historical values in the form of time-series and scenarios based on assumptions about the price-driving factors, to produce forecasted time-series for future Day-Ahead Market and Balancing Market prices.
In this scope, iWind has developed a tool for price forecasting using Neural Networks algorithms. It uses historical values in the form of time-series and scenarios based on assumptions about the price-driving factors, to produce forecasted time-series for future Day-Ahead Market and Balancing Market prices.
| iwind_prices_leaflet.pdf |
