Solar power forecasting using artificial neural networks

Can artificial neural network models improve PV power forecasting accuracy?

Over the years, advanced artificial neural network (ANN) models have been proposed to increase the accuracy of PV power forecasts for various geographical regions. Hence, this paper provides a state-of-the-art review of the five most popular and advanced ANN models for PV power forecasting.

Can artificial neural networks improve photovoltaic energy production?

Data recorded every minute over one year at an experimental photovoltaic plant revealed a strong correlation between energy production and the input variables. This research compared the performance of multilayer perceptron, feedforward, long short-term memory, and modular artificial neural networks architectures.

Are artificial neural networks useful for energy forecasting?

Artificial Neural Networks are a powerful aid to energy forecasting. This article explores the appropriate architecture and resolution algorithms. LSTM and modular models yield the best results for the problem under study.

Can artificial neural networks predict long-term output of a photovoltaic plant?

Forecasting long-term output of a photovoltaic plant is an unresolved challenge. Mitigating the uncertainty of energy production is crucial for its deployment. Artificial Neural Networks are a powerful aid to energy forecasting. This article explores the appropriate architecture and resolution algorithms.

Can a deep learning neural network estimate solar photovoltaic power?

De Jesús et al. proposed a hybrid deep learning neural network model for estimating solar photovoltaic power. The model was a blend of convolutional neural network (CNN) and long-short term memory (LSTM). The model’s input was historical PV power and weather data.

Can a neural network predict future output power values of solar cells?

Qasrawi and Awad implemented Multilayer Feed-Forward with Backpropagation Neural Networks to propose a model for predicting future output power values of solar cells. The model predicted the future output of solar cells accurately. Graditi et al. performed a comparative study on three methods for estimating power plant production.