Chung-Ang University Researchers Use Deep Learning to Develop a Forecasting Model for Efficiently Managing Electric Grids
It employs a long short-term memory network and incentive-based demand response program to predict uncertainties in renewable energy sources implemented in microgrids, their energy demand, and market prices.
Customer-Centric Energy Products to build the Solutions needed to Fight Climate Change
Despite rapid growth, the market is still so nascent that the prospective customer population is still largely unaware of the opportunity community solar provides.
8 Reasons to Integrate your Monitoring System with an Asset Management Software
Integration between solar and wind asset management software and monitoring software lets owners and operators automatically retrieve weather and production information, providing an up-to-date overview of the status of their portfolio.
Harnessing Alternative Data and Alternative Energy to Increase Efficiency
AI powered tools, like Dataminr, help firms make sense of the vast sets of social media data to confidently leverage information from on-the-ground events and enabling them to make the right decisions in the moment.
IBM Uses Machine Learning to Boost Solar for Dept of Energy
While other solar forecasting systems take more narrow location and timeframe views, whats different about the IBM approach is that we incorporate a great number of weather and solar energy prediction models. We then blend those using historical data as a function of weather situation, forecast horizon and location to create what we call a supermodel.
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