Short-term solar energy forecasting: Integrated computational intelligence of LSTMs and GRU

Abstract

Problems with erroneous forecasts of electricity production from solar farms create serious operational, technological, and financial challenges to both Solar farm owners and electricity companies. Accurate prediction results are necessary for efficient spinning reserve planning as well as regulating inertia and power supply during contingency events. In this work, the impact of several climatic conditions on solar electricity generation in Amherst. Furthermore, three machine learning models using Lasso Regression, ridge Regression, ElasticNet regression, and Support Vector Regression, as well as deep learning models for time series analysis include long short-term memory, bidirectional LSTM, and gated recurrent unit along with their variants for estimating solar energy generation for every five-minute interval on Amherst weather power station. These models were evaluated using mean absolute error root means square error, mean square error, and mean absolute percentage error. It was observed that horizontal solar irradiance and water saturation deficiency had a highly proportional relationship with Solar PV electricity generation. All proposed machine learning models turned out to perform well in predicting electricity generation from the analyzed solar farm. Bi-LSTM has performed the best among all models with 0.0135, 0.0315, 0.0012, and 0.1205 values of MAE, RMSE, MSE, and MAPE, respectively. Comparison with the existing methods endorses the use of our proposed RNN variants for higher efficiency, accuracy, and robustness. Multistep-ahead solar energy prediction is also carried out by exploiting hybrids of LSTM, Bi-LSTM, and GRU.

Photovoltaic power prediction method based on CNN-BIGRU-ATTENTION model

“…T t h  =   (11) where: t h is the output of the BIGRU layer, T w is the weight matrix,  is the output of the ATTENTION layer.…”

“…In reference [11], after using GRU to extract the temporal features of photovoltaic power generation, an attention mechanism was introduced to strengthen the focus on important information in the temporal input.…”

Predicting Smart Office Electricity Consumption in Response to Weather Conditions Using Deep Learning

“…GRU is frequently used when there is a requirement for LSTM performance, but at reduced computational expense. GRU uses gate mechanisms, such as gate reset and gate update, to regulate the flow of information within memory cells [45].…”

Two-Stage Neural Network Optimization for Robust Solar Photovoltaic Forecasting

“…In particular, DL models have received much attention due to their structure and learning method, which are excellent for processing data with complex patterns [20][21][22][23][24][25]. Abdel-Nasser and Mahmoud [20] developed a PV power prediction model using deep long short-term memory (LSTM) networks, which captures temporal dynamics with high accuracy.…”

“…Jiao et al [23] proposed a novel DL architecture for solar irradiance prediction that integrates convolutional graph neural networks with LSTM to improve accuracy and reliability for distributed PV systems. Zameer et al [24] proposed two DL models based on bidirectional LSTM (Bi-LSTM) and gated recurrent unit (GRU) to achieve superiority in short-term solar PV prediction over traditional methods such as lasso, ridge, elastic net, and SVM, highlighting the robustness and precision of DL. Rocha et al [25] conducted an in-depth analysis using LSTM, Bi-LSTM, and temporal convolutional network (TCN) for predicting solar PV generation on a 1320 watt-peak (Wp) amorphous plant, demonstrating the superior performance of TCN in terms of accuracy for both short-term (15-minute) and long-term (24-h) predictions.…”