AI is improving renewable energy forecasts

Описание: The renewable energy industry is the largest use case for AI. But forecasting generation and adding the demand side to integrate RE is a challenge. India’s 40 percent capacity and 22 to 23 percent of its generation come from RE. But we have to reach 500 gigawatts by 2030 and net zero by 2070; all of this would require integrating #RE.

Bullet Points:

#AI in Renewable Energy Industry

• AI is crucial for the renewable energy industry, particularly in forecasting generation and demand side integration.
• India's 40% capacity and 22-23% of its generation come from renewable energy (RE).
• The goal is to reach 500 gigawatts by 2030 and net zero by 2070, requiring integration of RE.

Long-term and Short-term Forecasting
• Long-term forecasting is crucial for project planning, investment commitments, and project bankability.
• Short-term forecasting is used for revenue and minimizing Deviation Settlement Mechanism (#DSM) penalties.
• AI provides intuitive models to establish relationships between multiple variables in determining the forecast.

Generation and Demand Side Forecast
• Generation and demand-side forecasts must be made on a 15-minute time block basis.
• AI provides machine learning models, deep learning models, and neural networks for forecasting.
• However, these models require training and data, presenting ongoing challenges.

Machine Learning Model
• A machine learning model was developed using data from the last three to four years to produce adjusted forecasts, improve QC forecasts, and reduce DSM penalties.
• The model identifies key factors across 96-time blocks in a day, predicting these factors and applying them to the forecasting agency’s inputs.

Potential Applications of Large AI Models
• Large AI models, including LLMs, can aid in large decision-making processes like predictive maintenance, overall generation forecasting, and load forecasting based on smart meters.
• AI models can also add value, reduce costs, or enhance revenue.

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