Situation-dependent blending of multiple forecasting models based on machine learning

18 November 2015, SPIE Newsroom. DOI: 10.1117/2.1201510.006142 Although the cost of renewable energy technologies has steadily decreased, the economic value of generating renewable energy has also decreased as it has become a larger fraction of the total energy mix.1 This trend undoubtedly poses a serious challenge for the further adoption of renewable energy. There are many underlying reasons for this, including the deterministic rules used today for power systems, which require highly predictable energy sources. In contrast, renewable energy is inherently intermittent, and there is effectively no viable method for large-scale energy storage to mitigate the intermittent output of renewable sources. Improved forecasting of the output of renewable energy is considered the best alternative to enable cost-effective grid integration, but this has so far been challenging because of the complexity of this…


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