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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. 
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