Machine learning techniques for solar power output predicting

Accurate solar power forecasting is necessary for solar power facilities to operate dependably and effectively. This thesis suggests a novel approach for making long-term forecasts regarding solar power output using Long Short-Term Memory (LSTM) neural networks and the Nadam optimizer. Despite the challenges brought on by the sun's erratic beams, the objective is to provide accurate projections for the system's design and operation.The research's initial phase involves using a variety of unique methodologies to compare and contrast the findings from numerous LSTM models with those from more traditional time series models like ARIMA and SARIMA. The suggested LSTM model using the Nadam optimizer generates more accurate predictions when compared to traditional methods. To increase the system's accuracy and dependability, the impact of climatic factors on solar power forecasting is being researched.The proposed approach results in a number of significant improvements. By taking into account the sensitivity of SPV output power to a variety of environmental conditions, it first presents a novel viewpoint on SPV power forecasting. By comparing the method to other widely used SPV power forecasting methodologies, it also confirms the technique's effectiveness. The recommended method facilitates in the prediction of mitigating factors like solar irradiance and SPV module efficiency in addition to increasing forecast accuracy.The limitations of the suggested technique are also highlighted in the research, including the need for suitable training data and the difficulties in managing the LSTM's forget gate's memory. Future research may look at different neural network topologies and integrate more input parameters to further boost prediction accuracy.Finally, this thesis presents a novel approach for long-term solar power forecasting that combines LSTM with the Nadam optimizer. The findings have ramifications for solar power system optimization, design, and operation, and they also help with the creation of solar power forecasting algorithms. Accurate estimates of solar power output enable improved system architecture, increased dependability, and increased economic viability. Key Words: Solar power forecasting, Long Short-Term Memory (LSTM), Nadam optimizer, Time series analysis, Meteorological parameters