Abstract:The penetrations of solar power plants are increasing their presence worldwide.The solar power plants have uncertain power output as its output depends on solar radiation, which is environmental dependent, so solar radiation prediction is a crucial step in integrating these plants into the power grid. In this work, a convolution neural network (CNN) and bi-direction long short term memory (BiLSTM) based hybrid deep learning (DL) model is proposed for effective midterm solar radiation prediction. The CNN archit… Show more
“…Figure 8 shows the annual average RMSE & MAPE comparison of this scenario. From these observations, It is clear that the BiLSTM network outperforms over LSTM & GRU as is also claimed in various previous studies (Li et al 2021 ; Rai et al 2021 ). # Scenario 2: Forecast using traditional WT and BiLSTM (WT-BiLSTM (T)) Fig.…”
Section: Simulation Resultssupporting
confidence: 83%
“…The CNN was used to extract the features of the input data time series; whereas, BiLSTM exploited the dependencies of the time series. This study proved that the CNN-BiLSTM model performed better than LSTM, GRU, CNN-LSTM and GRU-LSTM (Rai et al 2021 ). Therefore, inspired from the above work, this paper proposes an ensemble model to forecast 24-h ahead solar GHI using wavelet transform (WT) and BiLSTM with an objective to improve forecasting accuracy.…”
“…Figure 8 shows the annual average RMSE & MAPE comparison of this scenario. From these observations, It is clear that the BiLSTM network outperforms over LSTM & GRU as is also claimed in various previous studies (Li et al 2021 ; Rai et al 2021 ). # Scenario 2: Forecast using traditional WT and BiLSTM (WT-BiLSTM (T)) Fig.…”
Section: Simulation Resultssupporting
confidence: 83%
“…The CNN was used to extract the features of the input data time series; whereas, BiLSTM exploited the dependencies of the time series. This study proved that the CNN-BiLSTM model performed better than LSTM, GRU, CNN-LSTM and GRU-LSTM (Rai et al 2021 ). Therefore, inspired from the above work, this paper proposes an ensemble model to forecast 24-h ahead solar GHI using wavelet transform (WT) and BiLSTM with an objective to improve forecasting accuracy.…”
“…Types of distribution error such as skew and kurtosis are also considered in assessing the distribution of predicted solar radiation. The results show that the proposed model is more accurate than other recently proposed deep learning models [28].…”
Solar panel location assessment is usually a time-consuming manual process, and many criteria should be taken into consideration before deciding. One of the most significant criteria is the building location and surrounding environment. This research project aims to propose a model to automatically identify potential roof spaces for solar panels using drones and convolutional neural networks (CNN). Convolutional neural networks (CNNs) are used to identify buildings’ roofs from drone imagery. Transfer learning on the CNN is used to classify roofs of buildings into two categories of shaded and unshaded. The CNN is trained and tested on separate imagery databases to improve classification accuracy. Results of the current project demonstrate successful segmentation of buildings and identification of shaded roofs. The model presented in this paper can be used to prioritize the buildings based on the likelihood of getting benefits from switching to solar energy. To illustrate an implementation of the presented model, it has been applied to a selected neighborhood in the city of Hurricane in West Virginia. The research results show that the proposed model can assist investors in the energy and building sectors to make better and more informed decisions.
“…forced to learn more robust features that are useful in conjunction with many different random subsets of the other neurons". The possibility of inserting Dropout layers also between Pooling, CNN, or LSTM layers was explored, as done in [11,13], but the errors of the predictions were significantly higher. We had the lowest errors when using Dropout after the LSTM layers and between the Dense layers.…”
Section: Cnn-lstm Modelmentioning
confidence: 99%
“…In the following we briefly describe the evaluation metrics we used during the training and testing of our models. Apart from one custom metric we define, all the other functions are the standard ones used in the literature for similar data-filling problemssee for instance [4,6,7,10,11,30]. Three classical error metrics that we use are the Mean Absolute Error (MAE), the Mean Squared Error (MSE), and the Mean Absolute Percentage Error (MAPE).…”
In the process of collecting data from sensors, several circumstances can affect their continuity and validity, resulting in alterations of the data or loss of information. Although classical methods of statistics, such as interpolation-like techniques, can be used to approximate the missing data in a time series, the recent developments in Deep Learning (DL) have given impetus to innovative and much more accurate forecasting techniques. In the present paper, we develop two DL models aimed at filling data gaps, for the specific case of internal temperature time series obtained from monitored apartments located in Bolzano, Italy. The DL models developed in the present work are based on the combination of Convolutional Neural Networks (CNNs), Long Short-Term Memory Neural Networks (LSTMs), and Bidirectional LSTMs (BiLSTMs). Two key features of our models are the use of both pre-and post-gap data, and the exploitation of a correlated time series (the external temperature) in order to predict the target one (the internal temperature). Our approach manages to capture the fluctuating nature of the data and shows good accuracy in reconstructing the target time series. In addition, our models significantly improve the already good results from another DL architecture that is used as a baseline for the present work.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
