Neural networks with a good modeling capability have been used increasingly to predict
and forecast solar radiation. Even diverse application of neural network has been reported …

To better forecast solar variability, spatio-temporal methods exploit spatially distributed solar
time series, seeking to improve forecasting accuracy by including neighboring solar …

Solar radiation plays an important role in climate change, energy balance and energy
applications. In this work, an Adaptive Neuro-Fuzzy Inference Systems (ANFIS) is proposed …

The accurate forecasting of photovoltaic (PV) power generation is critical for smart grids and
the renewable energy market. In this paper, we propose a novel short-term PV forecasting …

Operating power systems with large amounts of renewables requires predicting future
photovoltaic (PV) production with fine temporal and spatial resolution. State-of-the-art …

This paper proposes and validates a data-driven minute-ahead forecast model for
photovoltaic (PV) generation, which is essential for real-time micro-grid scheduling. Unlike …

Since the availability of ground data is very sparse, satellite data provides an alternative
method to estimate solar irradiation. Satellite data across various spectral bands may be …

The use of renewable energies requires a precise and detailed quantification of the
resource available. Because of the cost of solar stations or limited availability of skilled …

Solar radiation data is highly desirable in various areas, such as agriculture, PV industries,
and building performance analysis. There are no commercial tools available for real-time …

Spatial data mining is a special type of data mining. The main difference between data
mining and spatial data mining is that in spatial data mining tasks we use not only non …