Presently, deep learning models are an alternative solution for predicting solar energy because of their accuracy. The present study reviews deep learning models for handling time-series data to predict solar irradiance and photovoltaic (PV) power. We selected three standalone models and one hybrid model for the discussion, namely, recurrent neural network (RNN), long short-term memory (LSTM), gated recurrent unit (GRU), and convolutional neural network-LSTM (CNN–LSTM). The selected models were compared based on the accuracy, input data, forecasting horizon, type of season and weather, and training time. The performance analysis shows that these models have their strengths and limitations in different conditions. Generally, for standalone models, LSTM shows the best performance regarding the root-mean-square error evaluation metric (RMSE). On the other hand, the hybrid model (CNN–LSTM) outperforms the three standalone models, although it requires longer training data time. The most significant finding is that the deep learning models of interest are more suitable for predicting solar irradiance and PV power than other conventional machine learning models. Additionally, we recommend using the relative RMSE as the representative evaluation metric to facilitate accuracy comparison between studies.

Solar irradiance fluctuates mainly due to clouds. A sky camera offers images with high temporal and spatial resolutions for a specific solar photovoltaic plant. The cloud cover from sky images is suitable for forecasting local fluctuations of solar irradiance and thereby solar power. Because no study applied deep learning for forecasting cloud cover using sky images, this study attempted to apply the long short-term memory algorithm in deep learning. Cloud cover data were collected by image processing of sky images and used for developing the deep learning model to forecast cloud cover 10 minutes ahead. The forecasted cloud cover data were plugged into solar radiation models as input in order to predict global horizontal irradiance. The forecasted results were grouped into three categories based on sky conditions: clear sky, partly cloudy, and overcast sky. By comparison with solar irradiance measurement at a ground station, the proposed model was evaluated. The proposed model outperformed the persistence model under high variability of solar irradiance such as partly cloudy days with relative root mean square differences for 10-minute-ahead forecasting are 25.10% and 39.95%, respectively. Eventually, this study demonstrated that deep learning can forecast the cloud cover from sky images and thereby can be useful for forecasting solar irradiance under high variability.

No abstract

No abstract

Decomposed solar radiation models are commonly used to separate direct and diffuse irradiance from global irradiance. However, most of these models are designed to process hourly data, which may not be sufficient to capture the rapid changes in solar irradiance that occur within a shorter timescale. To address this issue, we examined the performance of existing decomposition models at different temporal resolutions ranging from 1 min to 1 h. We found that the errors in the decomposition models increased as the temporal resolution decreased. Specifically, as the timescale was reduced from hourly to every minute, the relative root-mean-square error (rRMSE) increased by more than 5%. These findings highlight the need to develop accurate models that can process sub-hourly data. Accordingly, we propose the use of deep learning models to estimate the direct irradiance using sub-hourly data. The proposed models significantly reduced the rRMSE by more than 7% compared to the existing models on a 1-min time scale. The results indicate that deep-learning models can provide accurate estimates of direct irradiance, even at sub-hourly temporal resolutions.