Solar Power Prediction using Temporal Fusion Transformer

The need to transition toward renewable sources of power grows rapidly because rising world energy requirements continue to intensify the environmental conditions caused by fossil fuel reliance. The forecast of solar power generation serves as an essential element for maintaining stable grid operations and also enables superior management of distributed energy resources and storage units. Traditional statistical models alongside conventional machine learning approaches currently manage the solar power forecasting market while facing major implementation drawbacks. The models lack optimal capability to handle complex temporal patterns and weather adaptations fast enough or achieve dataset performance while making predictions that result in inaccurate decisions. This study attempts to resolve such deficiencies through the application of Temporal Fusion Transformer which serves as a deep learning method predominantly used in time-series forecasting. Because of its design this model combines short-term dependencies with exogenous factor integration and dynamic information weight adjustment system for better solar power forecasting results. The analysis seeks to achieve an outcome where training and validation on a special dataset result in Mean Absolute Error of 0.0471 and Mean Squared Error of 0.0033 and Root Mean Squared Error of 0.0574. This model reveals its value in actual energy management practice through its job-relatedness score which stands at 0.8983. The new method surpasses standard model limitations to transform forecasting accuracy measurement into a superior level. Application of this model will concentrate on developing scalability to perform long-term energy forecasts and trend assessments for establishing a sustainable and resilient energy infrastructure.