Hybrid energy system integration and management for solar energy: a review

The conventional grid is increasingly integrating renewable energy sources like solar energy to lower carbon emissions and other greenhouse gases. While energy management systems support grid integration by balancing power supply with demand, they are usually either predictive or real-time and therefore unable to utilise the full array of supply and demand responses, limiting grid integration of renewable energy sources. This limitation is overcome by an integrated energy management system. This review examines various concepts related to the integrated energy management system such as the power system configurations it operates in, and the types of supply and demand side responses. These concepts and approaches are particularly relevant for power systems that rely heavily on solar energy and have constraints on energy supply and costs. Building on from there, a comprehensive overview of current research and progress regarding the development of integrated energy management system frameworks, that have both predictive and real-time energy management capabilities, is provided. The potential benefits of an energy management system that integrates solar power forecasting, demand-side management, and supply-side management are explored. Furthermore, design considerations are proposed for creating solar energy forecasting models. The findings from this review have the potential to inform ongoing studies on the design and implementation of integrated energy management system, and their effect on power systems

A unique three-step weather data approach in solar energy prediction using machine learning

The importance of renewable energy sources like solar energy in reducing carbon emissions and other greenhouse gases has contributed to an increase in grid integration. However, the intermittent nature of solar power causes reliability issues and a loss of energy balance in the system, which are barriers to solar energy penetration. This study proposes a unique three-step approach that identifies weather parameters with moderate to strong correlation to solar radiation and uses them to predict solar energy generation. The combination of an on-site weather station and a reliable local weather station produces relevant data that increases the accuracy of the forecasting model irrespective of the machine learning algorithm used. This data source combination is tested, along with two other scenarios, using the exponential Gaussian Process Regression machine learning algorithm in MATLAB. It was found to be the most effective algorithm with a Normalized Root Mean Square Error of 1.1922, and an R2value of 0.66