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

Abstract

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.

A taxonomy of short‐term solar power forecasting: Classifications focused on climatic conditions and input data

“…The output layer gives the output predictive result based on the analysis of the hidden layers. The contributions of the works [30–56] that propose ANN‐based SPF are presented in Table 2.…”

Design and Development of a Short-Term Photovoltaic Power Output Forecasting Method Based on Random Forest, Deep Neural Network and LSTM Using Readily Available Weather Features

“…Given the strong correlation between the solar irradiance and the PV power output [16], many researchers attempt to develop new optimized irradiance forecasting methods. For instance, N. Michael et al [17] introduce a hybrid method for GHI and plane of array (POA) irradiance prediction trained on data collected from Sweihan Photovoltaic Independent Power Project in Abu Dhabi. The proposed solution combines a convolutional neural network (CNN) and a long shortterm memory neural network.…”

“…The proposed solution combines a convolutional neural network (CNN) and a long shortterm memory neural network. More specifically, the input data is processed first by CNN layers, which aim at detecting and extracting features from the observed data points [17]. The generated CNN output is forwarded to the LSTM network, which produces the final irradiance prediction.…”

“…In order to assess the performance of the proposed model, it is evaluated in terms of RMSE, MAE, R 2 and mean absolute percentage error. The resulting performance metrics indicate that the hybrid approach outperforms not only the standalone CNN and LSTM networks but also other traditional ML models such as SVR, artificial neural network (ANN) and linear regression (LR) [17].…”

Prediction of road dust concentration in open-pit coal mines based on multivariate mixed model

“…The LSTM model effectively solves the problems of gradient disappearance and gradient explosion in the recurrent neural network [ 39 ]. Still, in model training, the LSTM model continuously learns in the time order from front to back, so it can only know certain sample data information before a moment.…”