Energy generation forecasting: elevating performance with machine and deep learning

Distribution System Operators (DSOs) and Aggregators benefit from novel Energy Generation Forecasting (EGF) approaches. Improved forecasting accuracy may make it easier to deal with energy imbalances between production and consumption. It also aids operations such as Demand Response (DR) management in Smart Grid architecture. This work aims to develop and test a new solution for EGF. It combines various methodologies running EGF tests on historical data from buildings. The experimentation yields different data resolutions (15 min, one hour, one day, etc.) while reporting accuracy errors. The optimal forecasting technique should be relevant to a variety of forecasting applications in a trial-and-error manner, while utilizing different forecasting strategies, ensemble approaches, and algorithms. The final forecasting evaluation incorporates performance metrics such as coefficient of determination (R-2), Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), presenting a comparative analysis of results.

Automated summary

Distribution System Operators (DSOs) and Aggregators can benefit from improved Energy Generation Forecasting (EGF) approaches, which help deal with energy imbalances and support Demand Response (DR) management in Smart Grid architecture. This study aims to develop and test a new EGF solution by combining various methodologies and evaluating their performance using historical building data. The final forecasting evaluation includes performance metrics such as R-2, MAE, MSE, and RMSE to provide a comparative analysis of the results.