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Climate change projections for boreal winter precipitation in Tropical America has beenaddressed by statistical downscaling (SD) using the principal component regression with sea-level pressure (SLP) as the predictor variable. The SD model…
The non-stationarity characteristic of the solar power renders traditional point forecasting methods to be less useful due to large prediction errors. This results in increased uncertainties in the grid operation, thereby negatively…
When a territory is poorly instrumented, geostationary satellites data can be useful to predict global solar radiation. In this paper, we use geostationary satellites data to generate 2-D time series of solar radiation for the next hour.…
This paper contributes to the field of modeling and hindcasting of the total solar irradiance (TSI) based on different proxy data that extend further back in time than the TSI that is measured from satellites. We introduce a simple method…
A novel method for real-time solar generation forecast using weather data, while exploiting both spatial and temporal structural dependencies is proposed. The network observed over time is projected to a lower-dimensional representation…
This paper presents a machine learning-based approach for predicting solar power generation with high accuracy using a 99% AUC (Area Under the Curve) metric. The approach includes data collection, pre-processing, feature selection, model…
Among several heliophysical and geophysical quantities, the accurate evolution of the solar irradiance is fundamental to forecast the evolution of the neutral and ionized components of the Earth's atmosphere.We developed an artificial…
Context. There is no consensus on the amplitude of the historical solar forcing. The estimated magnitude of the total solar irradiance difference between Maunder minimum and present time ranges from 0.1 to 6 W/m2 making uncertain the…
Ahead-of-time forecasting of incident solar-irradiance on a panel is indicative of expected energy yield and is essential for efficient grid distribution and planning. Traditionally, these forecasts are based on meteorological physics…
In this paper, we demonstrate the importance of embedding temporal information for an accurate prediction of solar irradiance. We have used two sets of models for forecasting solar irradiance. The first one uses only time series data of…
Severity of warming predicted by climate models depends on their Transient Climate Response (TCR). Inter-model spread of TCR has persisted at ~100% of its mean for decades. Existing observational constraints of TCR are based on observed…
Electricity load consumption may be extremely complex in terms of profile patterns, as it depends on a wide range of human factors, and it is often correlated with several exogenous factors, such as the availability of renewable energy and…
Reliable uncertainty quantification is of critical importance in time series forecasting, yet traditional methods often rely on restrictive distributional assumptions. Conformal prediction (CP) has emerged as a promising distribution-free…
Accurate precipitation forecasting is essential for hydrometeorological risk management, especially for anticipating extreme rainfall that can lead to flash flooding and infrastructure damage. This study introduces a diffusion-based deep…
Accurate estimation of solar irradiance is essential for reliable modelling of solar photovoltaic (PV) power production. In Ireland's highly variable maritime climate, where ground-based measurement stations are sparsely distributed,…
The integration of renewable resources has increased in power generation as a means to reduce the fossil fuel usage and mitigate its adverse effects on the environment. However, renewables like solar energy are stochastic in nature due to…
Long prediction horizons in Model Predictive Control (MPC) often prove to be efficient, however, this comes with increased computational cost. Recently, a Robust Model Predictive Control (RMPC) method has been proposed which exploits models…
This paper proposes an anticipative transformer-based model for short-term solar irradiance forecasting. Given a sequence of sky images, our proposed vision transformer encodes features of consecutive images, feeding into a transformer…
For a "reference day" of minimal solar activity between cycles 23 and 24 we compute the brightness temperature from solar spectral irradiance for each wavelength. We consider small variations of irradiance and temperature about the…
As photovoltaic power is expanding rapidly worldwide, it is imperative to assess its promise under future climate scenarios. While a great deal of research has been devoted to trends of mean solar radiation, less attention has been paid to…