Stacked Deep Learning LSTM Model for Daily Solar Power Time Series Forecasting
Several researches have been concentrated on predicting issues in many appl cation areas. In machine learning field, recurrent neural networks have been successfully employed. Such models were suggested to tackle time dependent learning problems. The goal of this paper is to afford a credible forecasting methodology for solar power, which will reduce the inaccuracies involved in forecasting the near future generation. In this proposed methodology, the stacked LSTM models with different configurations are applied to track the dynamic performance of the solar energy. The second objective of this analysis is to assess the effect of the stacked LSTM hidden layers by comparing the predicted outcomes of each model. We evaluate the proposed approach with three forecasting algorithms. The simulation results show that the value of the MAPE, using stacked LSTM model, starts from 10.5% (one hidden layer) and decreases to 0.442% when four hidden layers are applied. Thus, the use of Stacked LSTM with multiple layers provides a further decrease in the fore casting error relative to other approaches.
Digital Technologies and Applications