Abstract
The installed capacity of photovoltaic power generation occupies an increasing proportion in the power system, and its stability is greatly affected by the fluctuation of solar radiation. Accurate prediction of solar radiation is an important prerequisite for ensuring power grid security and electricity market transactions. The current mainstream solar radiation prediction method is the deep learning method, and the structure design and data selection of the deep learning method determine the prediction accuracy and speed of the network. In this paper, we propose a novel long short-term memory (LSTM) model based on the attention mechanism and genetic algorithm (AGA-LSTM). The attention mechanism is used to assign different weights to each feature, so that the model can focus more attention on the key features. Meanwhile, the structure and data selection parameters of the model are optimized through genetic algorithms, and the time series memory and processing capabilities of LSTM are used to predict the global horizontal irradiance and direct normal irradiance after 5, 10, and 15 min. The proposed AGA-LSTM model was trained and tested with two years of data from the public database Solar Radiation Research Laboratory site of the National Renewable Energy Laboratory. The experimental results show that under the three prediction scales, the prediction performance of the AGA-LSTM model is below 20%, which effectively improves the prediction accuracy compared with the continuous model and some public methods.
Highlights
Introduction tral with regard to jurisdictionalIn recent years, the demand for energy consumption has increased, followed by a series of problems such as energy shortages and environmental degradation
In order to further analyze the performance of the proposed model, the relative error distributions of genetic algorithm (GA)‐long short‐term memory (LSTM) and AGA‐LSTM for predicting Global horizontal irradiance (GHI) under three prediction scales were shown as Figure 4
We propose an AGA‐LSTM model to predict GHI and direct normal irradiance (DNI) at 5, 10, and 15 minute time steps
Summary
The demand for energy consumption has increased, followed by a series of problems such as energy shortages and environmental degradation. Based on the data‐driven solar irradiance prediction method, the mapping relation‐. In response to the above problems, we proposed a prediction model of long short‐term memory (LSTM) based on an attention mechanism (AM) and a genetic algo‐. Tion, the GA is used to perform a global search on the model parameters to obtain the optimal solution, and the optimal parameter combination is used to build the LSTM model, which integrates various meteorological variable data to predict the GHI and DNI after 5, 10, and 15 minutes. The main contributions of this paper are as follows: Introduce the AM to determine the influence degree of different features on the target predicted value, so that the model can focus on important variables; and Energies 2022, 15, 1062.
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