Solar Radiation Prediction Based on Convolution Neural Network and Long Short-Term Memory

Zhu, Tingting; Guo, Yiren; Li, Zhenye; Wang, Cong

doi:10.3390/en14248498

Cited by 31 publications

(19 citation statements)

References 33 publications

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“…A very-short term intra hour 10 minutes ahead forecasting of solar irradiation by using a combination of total sky images and historical data is presented in [8]. The proposed model is a Siamese Convolutional Neural Network -Long Short-Term Memory (SCNN -LSTM).…”

Section: B Literature Review: Cnn-lstm and Lstm Bi-directional Modelsmentioning

confidence: 99%

Probabilistic LSTM-Autoencoder Based Hour-Ahead Solar Power Forecasting Model for Intra-Day Electricity Market Participation: A Polish Case Study

et al. 2022

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This article presents the selection of an appropriate deep learning Long Short-Term Memory (LSTM) based probabilistic hour-ahead forecasting model for a grid connected industrial solar PV power plant located in Poland. It has a 317 kW peak power capacity and is connected with a metallurgical plant producing steel for car parts. The purpose of the study is to present a model that could be used by the plant to participate in the Polish intra-day electricity market. Four different LSTM models were investigated which include the vanilla model, the stacked LSTM model, the Bi-directional LSTM model and the LSTM-Autoencoder model. Out of the investigated models it was observed that the LSTM-Autoencoder model was the best performing one in terms of reliability. The average Root Mean Squared Error (RMSE) and the average Mean Absolute Error (MAE) for the Autoencoder model over 100 runs were 15.59 kW and 8.36 kW which represent 4.9% and 2.6% of the peak power respectively. Moreover, it was observed that it has the shortest width for the 95% confidence interval of only 0.5% for both the RMSE and the MAE. In terms of accuracy the best performing model was the LSTM bi-directional model with the average RMSE and MAE values of 12.87 kW and 6.91 kW which represent 4% and 2.1% of the peak power. The 95% confidence intervals width for both the RMSE and MAE over the 100 runs were 0.8% and 0.5% respectively.INDEX TERMS Solar power forecasting, LSTM, deep learning, electricity market and grid connected solar PV.

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Section: B Literature Review: Cnn-lstm and Lstm Bi-directional Modelsmentioning

confidence: 99%

Probabilistic LSTM-Autoencoder Based Hour-Ahead Solar Power Forecasting Model for Intra-Day Electricity Market Participation: A Polish Case Study

et al. 2022

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“…If the data is multiplied by one, the value remains the same; if the data is multiplied by zero, the value becomes zero and disappears. There are three types of gates [14,[46][47][48][49][50]:…”

Section: Long Short-term Memory (Lstm) Networkmentioning

confidence: 99%

“…-Output Gate: the next hidden state is set in the output gate. The sigmoid output has to be multiplied by the tanh function; the result of this multiplication decides which There are three types of gates [14,[46][47][48][49][50]:…”

Section: Long Short-term Memory (Lstm) Networkmentioning

confidence: 99%

Solar Radiation Forecasting by Pearson Correlation Using LSTM Neural Network and ANFIS Method: Application in the West-Central Jordan

et al. 2022

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Solar energy is one of the most important renewable energies, with many advantages over other sources. Many parameters affect the electricity generation from solar plants. This paper aims to study the influence of these parameters on predicting solar radiation and electric energy produced in the Salt-Jordan region (Middle East) using long short-term memory (LSTM) and Adaptive Network-based Fuzzy Inference System (ANFIS) models. The data relating to 24 meteorological parameters for nearly the past five years were downloaded from the MeteoBleu database. The results show that the influence of parameters on solar radiation varies according to the season. The forecasting using ANFIS provides better results when the parameter correlation with solar radiation is high (i.e., Pearson Correlation Coefficient PCC between 0.95 and 1). In comparison, the LSTM neural network shows better results when correlation is low (PCC in the range 0.5–0.8). The obtained RMSE varies from 0.04 to 0.8 depending on the season and used parameters; new meteorological parameters influencing solar radiation are also investigated.

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“…Hence, to increase the accuracy of the prediction model, hybridization of these DL models was also successfully carried out by different researchers for different applications, for example, CNN-LSTM [19] (short-term load forecasting model), LSTM-RNN [20] (shortterm solar forecasting), etc. The output results corresponding to these hybrid models shows an incremental accuracy level compared to solo techniques.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique

et al. 2022

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Variability in solar irradiance has an impact on the stability of solar systems and the grid’s safety. With the decreasing cost of solar panels and recent advancements in energy conversion technology, precise solar energy forecasting is critical for energy system integration. Despite extensive research, there is still potential for advancement of solar irradiance prediction accuracy, especially global horizontal irradiance. Global Horizontal Irradiance (GHI) (unit: KWh/m2) and the Plane Of Array (POA) irradiance (unit: W/m2) were used as the forecasting objectives in this research, and a hybrid short-term solar irradiance prediction model called modified multi-step Convolutional Neural Network (CNN)-stacked Long-Short-Term-Memory network (LSTM) with drop-out was proposed. The real solar data from Sweihan Photovoltaic Independent Power Project in Abu Dhabi, UAE is preprocessed, and features were extracted using modified CNN layers. The output result from CNN is used to predict the targets using a stacked LSTM network and the efficiency is proved by comparing statistical performance measures in terms of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MAE), and R2 scores, with other contemporary machine learning and deep-learning-based models. The proposed model offered the best RMSE and R2 values of 0.36 and 0.98 for solar irradiance prediction and 61.24 with R2 0.96 for POA prediction, which also showed better performance as compared to the published works in the literature.

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Solar Radiation Prediction Based on Convolution Neural Network and Long Short-Term Memory

Cited by 31 publications

References 33 publications

Probabilistic LSTM-Autoencoder Based Hour-Ahead Solar Power Forecasting Model for Intra-Day Electricity Market Participation: A Polish Case Study

Probabilistic LSTM-Autoencoder Based Hour-Ahead Solar Power Forecasting Model for Intra-Day Electricity Market Participation: A Polish Case Study

Solar Radiation Forecasting by Pearson Correlation Using LSTM Neural Network and ANFIS Method: Application in the West-Central Jordan

Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique

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