Prediction of Power Generation of a Photovoltaic Power Plant Based on Neural Networks

Abstract

Photovoltaic energy production is an important factor for increasing the electricity supply. The ability to predict the electric power production (EPP) of a photovoltaic (PV) farm supports from the management process of the power grid to the trade in the energy market and much more. Also, by predicting the production of PV power (PVP), it is possible to monitor the lifetime of the solar cells that form the backbone of any solar PV system. As a critical result, sudden failures of the PV plant can be avoided. Using a long short-term memory recurrent neural network (LSTM-RNN) model, this work evaluates the prediction accuracy of two forecasting strategies: the recursive strategy and the non-recursive Multiple-Input and Multiple-Output, respectively. The dataset consists of 5-years in-filed production data measurements collected from the CETATEA photovoltaic power plant, a research site facility for renewable energies located in Cluj-Napoca, Romania. The high granularity of the electric power production dataset values recorded each 1 hour guarantees the overall prediction accuracy of the system. The impact of the dataset size, the number of previous observations, and the forecast horizon on the neural network prediction accuracy is evaluated for each strategy. The performance metrics used to evaluate the prediction accuracy are the root mean square error, the mean bias error, and the mean average error. The results analysis demonstrates the ability of the implemented machine learning models to predict electric power production, as well as their importance in the energy loss management process.

Probability Calculation for Utilization of Photovoltaic Energy in Electric Vehicle Charging Stations

“…In ref. [33], the authors deal with the prediction of PV power generation using Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) models. They assess the accuracy of two forecasting strategies: recursive strategy and non-recursive multiple-input and multiple-output.…”

“…Additionally, researchers are investigating the usage of hybrid models that combine aspects of physical, statistical, and machine learning approaches to determine the limitations of individual methods. Provides promising results in the area of production prediction [32] PV plant operation forecasts based on short-term forecasts Naive persistent, autoregressive, and autoregressive moving average models Provides a multi-level hierarchical system for solar power plant production planning [33] Predicting PV power plant generation using LSTM-RNN…”

A Novel Improved Variational Mode Decomposition-Temporal Convolutional Network-Gated Recurrent Unit with Multi-Head Attention Mechanism for Enhanced Photovoltaic Power Forecasting

“…As the scale of power plants continues to expand, the amount of data produced by power plants has also exploded. In fact, due to the quantity and quality of the source data of power plants, the traditional neural network photovoltaic power forecasting model is restricted by not considering environmental factors [22], thereby lacking reasonable utilization of complex sequence information. In addition, considering the nonlinear change in photovoltaic power and multiple environment sequence information, the convergence rate of the model slows down and overfitting appears with the increase in network input variables [23][24][25].…”

Research on genetic algorithm back propagation neural network photovoltaic daily power prediction system

“…References [6] utilizes the high adaptability of wavelet functions to establish a wavelet neural network prediction system based on the back propagation (BP) neural network algorithm. References [7] combines recursive neural network and Bayesian regularization algorithm for photovoltaic power prediction. However, the random initialization of weights and thresholds in neural networks as well as slow training speed greatly limits the accuracy and reliability of predictions.…”