Solar Power Prediction using LTC Models

Renewable energy production has been increasing at a tremendous rate in the past decades. This increase led to various benefits such as low cost of and making independent fossil fuels. However, order fully reap renewable produce an optimum manner, it is essential that we forecast production. Historically deep learning-based techniques have successful accurately forecasting solar In this paper develop ensemble model utilizes ordinary differential based neural networks (Liquid Time constant Networks Recurrent Neural networks) power 24 hours ahead. Our able achieve superior result with MAPE 5.70% MAE 1.07 MW.