AN IMPROVED ACCURACY FOR THE FORECASTING OF POWER GENERATION OVER A LONG-TERM HORIZON
Abstract
Renewable energy becomes increasingly popular in the global electric energy grid, improving the accuracy of renewable energy forecasting is critical to power system planning, management, and operations. However, this is a challenging task due to the intermittent and chaotic nature of renewable energy data. To date, various methods have been developed, including physical models, statistical methods, artificial intelligence techniques, and their hybrids to improve the forecasting accuracy of renewable energy. Hence this research proposed to hybridize two strong deep learning algorithms where modeling of more complex functioning is allowed by the use of multiple layers of abstraction in order to come up with a powerful forecasting model to predict solar power generation over long term horizon. Finally, the Deep Neutral Network and Long-short Term memory Network (DNN-LSTM) method can generate predicted solar energy consumption in a fully connected hierarchy. The proposed DNN-LSTM model achieved Mean Square Error (MSE) of 0.00825 and MAE of 0.00100 respectively. This is by far the lowest value when compare against the existing model i.e MLSHM which has MSE of 0.05700 and MAPE of 0.00695, LSTM which has MSE of 0.0536 and MAE of 0.0037 and Gated Recurrent Unit (GRU) which has MSE of 0.03460 and MAE of 0.00243 respectively. Thus, the proposed DNN-LSTM have clearly enhanced the forecasting accuracy as against all the existing models that was used for the evaluation and achieved the lowest values in terms of validation of MSE and MAE.
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