Modelling of photovoltaic system power prediction based on environmental conditions using neural network single and multiple hidden layers

Abstract

The solar power plant is an alternative to the provision of environmentally friendly renewable electricity, especially in the tropics, which are sufficiently exposed to the sun throughout the year. However, environmental conditions such as rainfall, solar radiation, or clouds may affect the output power of photovoltaic (PV) systems. These factors make it difficult to know whether PV can meet the needs of the existing load. This research develops a model to predict the output power of a 160 x 285W PV system located in the tropics and has certain environmental conditions. The prediction development is supported by the Python programming language with a single hidden layer and two hidden layers Neural Network, as well as the traditional Multiple Linear Regression tools. The simulation results show that the two hidden layers Neural Network method has a higher level of accuracy compared to the single hidden layer and Multiple Linear Regression as seen from the value of R2, MSE, and MAE.

A Short-Term Solar Photovoltaic Power Optimized Prediction Interval Model Based on FOS-ELM Algorithm

“…To help forecast generation, the Python language is employed, together with one layer and multiple hidden layer multilayer perceptron, as well as typical multiple linear regression approaches. The simulation outcomes indicate that the neural network approach with two hidden layers surpasses one hidden layer and more linear regression in terms of reliability, as evaluated by R2, MSE, and MAE values [12]. Cervone et al provide a method for creating 72-hour stochastic and deterministic estimates of the power produced by the photovoltaic (PV) power plants by utilizing input from the climate estimate, and quantifiable astronomical factors are based on Artificial Neural Networks (ANN) and Analog Ensemble (AnEn).…”