This technical research paper by SmartHelio focuses on how to effectively combine climate science, satellite imagery/data, global meteorological databases, Artificial Intelligence, Machine Learning, and Deep-Learning to improve the accuracy of solar and wind resource forecasting. Read to know more.

Solar PV plants face some pertinent challenges today. Unpredictable and fluctuating future solar irradiance and wind speed are one of them. The present solar and wind forecasting services are unable to provide accurate predictions. Thus, in the absence of standard forecasting projections, renewable energy developers are not able to plan the future production, operations and maintenance of clean energy assets in a better way. Furthermore, it adversely impacts the investability and credibility of the existing and upcoming renewable energy projects, reducing their overall share in the clean energy market. As a result,  renewable energy utilities have to bear the burden of hefty penalties for not being able to meet their energy commitments.

In our technical research paper on advance GHI Forecasting, we have attempted to work on a solution to the above problem by considering factors like, the inter-day and intra-day variability of the renewable energy resources, human or anthropogenic and local environmental factors, Global climatic events, Climate Change and the impact of different factors and the training models (statistical, machine learning, and deep learning). During our research and testing we blended climate science, satellite imagery/data, global meteorological databases, AI, ML, and Deep-Learning to take the predictions to the next level of forecasting accuracy band. 

Finally after careful validation, we developed an AI-based framework to automatically select the best models, factors, and their combinations to optimize the overall accuracy of the predictions. The results were equal to more than 98% accurate. 

To read more about our AI-based framework click on the button below and download our Technical Research paper on ‘Resource Forecasting’.