AI-Optimized Renewable Energy Forecasting

As renewable energy sources are increasingly incorporated into power grids, genuine forecasting of generation is necessary in order to avoid instability of grids and energy loss, among other things. In this research we are exploring the Deep Learning Transformer Network with Google Cloud AutoML for AI optimized renewable energy forecasting approach. Transformer model, which can capture long range dependencies in time series data, helps in improving predictive performance but also Google Cloud AutoML automates model selection and hyperparameter guessing removing the need of during extensive human involvement. Experimental results yield improved precision of forecasting as compared to the traditional machine learning methods. Through this AI driven methodology, energy providers, grid operators, policymakers aim for a more sustainable energy future have a scalable and reliable solution.