Leveraging Artificial Intelligence, Machine Learning, and Cloud-Based IT Integrations to Optimize Solar Power Systems and Renewable Energy Management

The global energy industry is experiencing a rapid energy transformation, changing the way we produce, manage, and consume energy. Electricity is a rapidly growing segment of the global energy system and the key enabler of decarbonization across several different sectors. An excess of renewable energy can lead to grid instability by making it difficult for the operator to balance electricity demand and generation. Market forces can enforce this balance, but these market solutions are often slow to deploy, reduce energy revenues, and can lead to curtailment rather than direct usage of excess sun. With energy forecasting and real-time analytics, the system can manage the dispatch of energy events, dynamically balancing load, generation, and energy storage. To extend system observability, machine learning-based forecasting and classification using telemetered grid conditions provide batch and real-time algorithms to evaluate grid conditions and outages.