Related papers: Development of a hybrid machine-learning and optim…
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…
This study predicts hourly solar irradiance components, Global Horizontal Irradiance (GHI), Direct Normal Irradiance (DNI), and Diffuse Horizontal Irradiance (DHI) using meteorological data to forecast solar energy output in Ibadan,…
We propose a two-stage multi-objective optimization framework for full scheme solar cell structure design and characterization, cost minimization and quantum efficiency maximization. We evaluated structures of 15 different cell designs…
Agrivoltaic systems are becoming more popular as a critical technology for attaining several sustainable development goals such as affordable and clean energy, zero hunger, clean water and sanitation, and climate action. However,…
Satellite-based solar irradiation forecasting is useful for short-term intra-day time horizons, outperforming numerical weather predictions up to 3-4 hours ahead. The main techniques for solar satellite forecast are based on sophisticated…
Drones have become indispensable assets during human-made and natural disasters, offering damage assessment, aid delivery, and communication restoration capabilities. However, most drones rely on batteries that require frequent recharging,…
In order to utilize solar imagery for real-time feature identification and large-scale data science investigations of solar structures, we need maps of the Sun where phenomena, or themes, are labeled. Since solar imagers produce…
Sky-image-based solar forecasting using deep learning has been recognized as a promising approach in reducing the uncertainty in solar power generation. However, one of the biggest challenges is the lack of massive and diversified sky image…
In Chinese building codes, it is required that residential buildings receive a minimum number of hours of natural, direct sunlight on a specified winter day, which represents the worst sunlight condition in a year. This requirement is a…
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,…
A number of industrial applications, such as smart grids, power plant operation, hybrid system management or energy trading, could benefit from improved short-term solar forecasting, addressing the intermittent energy production from solar…
Solar flares are among the most powerful and dynamic events in the solar system, resulting from the sudden release of magnetic energy stored in the Sun's atmosphere. These energetic bursts of electromagnetic radiation can release up to…
Machine learning has emerged as a promising approach for estimating material parameters in solar cells. Traditional methods for parameter extraction often rely on time-consuming numerical simulations that fail to capture the full complexity…
Over the past few decades, many applications of physics-based simulations and data-driven techniques (including machine learning and deep learning) have emerged to analyze and predict solar flares. These approaches are pivotal in…
The volume of data being collected in solar physics has exponentially increased over the past decade and with the introduction of the $\textit{Daniel K. Inouye Solar Telescope}$ (DKIST) we will be entering the age of petabyte solar data.…
Recently, there has been growing interest in the use of machine-learning methods for predicting solar flares. Initial efforts along these lines employed comparatively simple models, correlating features extracted from observations of…
Advancing probabilistic solar forecasting methods is essential to supporting the integration of solar energy into the electricity grid. In this work, we develop a variety of state-of-the-art probabilistic models for forecasting solar…
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…
Electric machine design optimization is a computationally expensive multi-objective optimization problem. While the objectives require time-consuming finite element analysis, optimization constraints can often be based on mathematical…
Optimization is becoming a crucial element in industrial applications involving sustainable alternative energy systems. During the design of such systems, the engineer/decision maker would often encounter noise factors (e.g. solar…