How is PV power generation forecasting based on climatic data?

PV power generation forecasting is long-term by considering climatic data such as solar irradiance, temperature and humidity. Moreover, we implemented these deep learning methods on two datasets, the first one is made of electrical consumption data collected from smart meters installed at consumers in Douala.

What is solar photovoltaic (PV)?

Solar photovoltaic (PV) is an increasingly significant fraction of electricity generation. Efficient management, and innovations such as short-term forecasting and machine vision, demand high-resolution geographic datasets of PV installations.

What are some recent developments in solar PV power forecasting?

Other studies, such as that of Gupta and Singh , have reviewed recent developments in solar PV power forecasting. They emphasized research that uses ML techniques built and considered different forecast horizons and multiple input parameters.

What data is collected from a low-voltage substation?

This dataset contains voltage, current, power, energy, and weather data from low-voltage substations and domestic premises with high uptake of solar photovoltaic (PV) embedded generation. Data collected as part of the project run by UK Power Networks.

How to predict power production of photovoltaic module using ambient weather conditions?

Prediction of power production of photovoltaic module considering ambient weather conditions. Predictive models have been developed using both artificial neural network and regression analysis. Solar irradiation, ambient and module temperature are key factors and important variables to estimate PV power generation.

Can deep learning predict photovoltaic power generation?

The deep learning methods applied for photovoltaic power generation forecasting include BP, LSTM, GRU, and Elman neural networks. Zhang et al. 9 used a 3-layer BP neural network to learn from historical data, and the model's predictions were highly accurate.