Application of AI for Short-Term PV Generation Forecast

Abstract

The efficient use of the photovoltaic power requires a good estimation of the PV generation. That is why the use of good techniques for forecast is necessary. In this research paper, Long Short-Term Memory, Bidirectional Long Short-Term Memory and the Temporal convolutional network are studied in depth to forecast the photovoltaic power, voltage and efficiency of a 1320 Wp amorphous plant installed in the Technology Support Centre in the University Rey Juan Carlos, Madrid (Spain). The accuracy of these techniques are compared using experimental data along one year, applying 1 timestep or 15 min and 96 step times or 24 h, showing that TCN exhibits outstanding performance, compared with the two other techniques. For instance, it presents better results in all forecast variables and both forecast horizons, achieving an overall Mean Squared Error (MSE) of 0.0024 for 15 min forecasts and 0.0058 for 24 h forecasts. In addition, the sensitivity analyses for the TCN technique is performed and shows that the accuracy is reduced as the forecast horizon increases and that the 6 months of dataset is sufficient to obtain an adequate result with an MSE value of 0.0080 and a coefficient of determination of 0.90 in the worst scenarios (24 h of forecast).

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.…”

“…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.…”

Short-Term Forecast of Photovoltaic Solar Energy Production Using LSTM

“…An author [3] studied the performance of using LSTM, bidirectional LSTM (BiLSTM), and a temporal convolutional network (TCN) for predicting the power of a photovoltaic solar power plant at the Technical Support Centre of Rey Juan Carlos University (Madrid, Spain). They used one year of data from the plant sampled every 15 min to predict the corresponding power, efficiency, and voltage with a horizon of 15 min and 24 h. The TCN network showed better forecasting results compared to the other networks, and the BiLSTM showed better results compared to the LSTM in terms of the mean squared error (MSE) indicator.…”

A Comprehensive Review of Supervised Learning Algorithms for the Diagnosis of Photovoltaic Systems, Proposing a New Approach Using an Ensemble Learning Algorithm

“…For example, Amiri et al proposed a Deep Learning algorithm that combines convolutional and bidirectional recurrent neural networks to detect faults in a PV system [19]. Additionally, several authors have conducted reviews to highlight the effectiveness of Machine Learning and Deep Learning algorithms in diagnosing PV systems, as they accelerate and improve diagnostic solutions for PV systems [20][21][22][23][24][25][26][27]. This article specifically focuses on supervised machine learning algorithms.…”