AI for Renewable Energy Optimization

Renewable energy is highly volatile and depends on weather conditions. Globally, there is a push towards the use of enhanced weather monitoring and prediction system to maximize the output from renewable energy (RE) sources. Countries have set up ambitious target to increase the energy from renewables to over 30% by 2030, and close to net-zero emissions by 2050 to tackle climate change.

Accurate and reliable weather forecast is therefore important, as it plays a major role in every phase of the renewable energy lifecycle, from site selection to managing demand and supply. The weather data from multiple meteorological sources are used to develop statistical and AI-based models for both short and long-term predictions to optimize RE generation, such as from solar and wind energy. AI-based weather prediction is gaining popularity over traditional weather modelling. AI models use neural network programming for wind forecasts to determine wind speed, direction and future availability. Solar farms use AI to predict solar irradiance, temperature, wind speed, humidity and cloud cover, which are factors affecting the generation efficiency. Using advanced weather models, RE generators can maximise power output and ensure better reliability of service.

Government regulations also impact profitability of RE generators, as failing to…