Photovoltaic systems have been widely deployed in recent times to meet the increased electricity demand as an environmental-friendly energy source. The major challenge for integrating photovoltaic systems in power systems is the unpredictability of the solar power generated. In this paper, we analyze the impact of havi…
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29 results for “photovoltaics”
The performance of an organic photovoltaic device is intricately connected to its active layer morphology. This connection between the active layer and device performance is very expensive to evaluate, either experimentally or computationally. Hence, designing morphologies to achieve higher performances is non-trivial …
The ability to accurately forecast power generation from renewable sources is nowadays recognised as a fundamental skill to improve the operation of power systems. Despite the general interest of the power community in this topic, it is not always simple to compare different forecasting methodologies, and infer the imp…
A new learning framework reduces PV-Battery system costs by 3.6%.
problem Optimizing PV-Battery systems for cost savings with accurate forecasts.
method Decision-focused learning framework integrating optimization and prediction.
result Decision-focused method reduces average electricity costs by 3.6%.
Deep learning model forecasts PV power production with high accuracy.
problem Accurate forecasting of photovoltaic power production is needed for electricity infrastructure.
method Sequence to sequence model with attention mechanism using weather predictions and historical data.
result The model outperforms existing methods in forecast skill score.
We show that different rates should be used for borrowing and discount rates, and that the risk-free rate should be used for discounting when assessing and comparing the cost of energy accross diffferent producers and technologies, on the example of photovoltaics. Recent quantitative models using the same rate for borr…
In this paper, we present a method to determine the global horizontal irradiance (GHI) from the power measurements of one or more PV systems, located in the same neighborhood. The method is completely unsupervised and is based on a physical model of a PV plant. The precise assessment of solar irradiance is pivotal for …
Develops framework for valuing and assessing risk of renewable PPAs.
problem Valuation and risk assessment of non-standard renewable PPAs.
method Formalizes payoff structures, derives fair contract prices, proposes market risk-assessment methodology.
result Fair prices and risk profiles vary across technologies and contractual structures.
This paper presents a novel and flexible solution for fault prediction based on data collected from SCADA system. Fault prediction is offered at two different levels based on a data-driven approach: (a) generic fault/status prediction and (b) specific fault class prediction, implemented by means of two different machin…
Cold-start PV forecasting uses synthetic histories to train time-series foundation models.
problem Cold-start PV forecasting
method Zero-shot pipeline with synthetic histories
result TabPFN-TS achieves the lowest error under Real Feedback strategy
Molecular structure-property relationships are key to molecular engineering for materials and drug discovery. The rise of deep learning offers a new viable solution to elucidate the structure-property relationships directly from chemical data. Here we show that the performance of graph convolutional networks (GCNs) for…
Paper uses conformal prediction for solar power forecasting in electricity markets.
problem Enhancing participation in electricity markets through accurate day-ahead PV power predictions.
method Combines machine learning for point predictions and conformal prediction for uncertainty quantification.
result CP with k-nearest neighbors and Mondrian binning outperforms linear quantile regressors in predicting PV power.
Improved prediction of polymer morphology through machine learning and simulations.
problem Understanding and predicting the morphology of multi-component polymer blends.
method Modified Cahn-Hilliard model for simulations, machine learning for clustering and prediction.
result Machine learning achieved 90% accuracy in predicting polymer morphology.
Framework improves PV forecasting by accounting for missing data uncertainty.
problem Uncertainty from missing data in PV power data.
method Combines stochastic multiple imputation with Rubin's rule.
result Improves prediction interval calibration without sacrificing point prediction accuracy.
As renewable distributed energy resources (DERs) penetrate the power grid at an accelerating speed, it is essential for operators to have accurate solar photovoltaic (PV) energy forecasting for efficient operations and planning. Generally, observed weather data are applied in the solar PV generation forecasting model w…
Effective utilization of photovoltaic (PV) plants requires weather variability robust global solar radiation (GSR) forecasting models. Random weather turbulence phenomena coupled with assumptions of clear sky model as suggested by Hottel pose significant challenges to parametric & non-parametric models in GSR conversio…
Traditional load analysis is facing challenges with the new electricity usage patterns due to demand response as well as increasing deployment of distributed generations, including photovoltaics (PV), electric vehicles (EV), and energy storage systems (ESS). At the transmission system, despite of irregular load behavio…
ABO3 Perovskites' Formability Prediction and Crystal Structure Classification using Machine Learningcond-mat.mtrl-sci
Machine learning predicts perovskite formability and classifies crystal structures.
problem Predicting and classifying perovskite formability and crystal structures.
method Machine learning, specifically Random Forest, with 5-fold cross-validation.
result 98.57% accuracy in predicting perovskite formability and 90.53% in classifying crystal structures.
Machine Learning for Quantum Dynamics: Deep Learning of Excitation Energy Transfer Propertiesphysics.chem-ph
Understanding the relationship between the structure of light-harvesting systems and their excitation energy transfer properties is of fundamental importance in many applications including the development of next generation photovoltaics. Natural light harvesting in photosynthesis shows remarkable excitation energy tra…
Study improves prediction accuracy and uncertainty for mobile sensor data using randomized neural networks.
problem Improving prediction accuracy and uncertainty for mobile sensor data.
method Cross-validation and uncertainty determination for randomized neural networks.
result Improved out-of-sample performance and confidence intervals for prediction error.
Distribution grids currently lack comprehensive real-time metering. Nevertheless, grid operators require precise knowledge of loads and renewable generation to accomplish any feeder optimization task. At the same time, new grid technologies, such as solar photovoltaics and energy storage units are interfaced via invert…
Dynamic Energy Dispatch Based on Deep Reinforcement Learning in IoT-Driven Smart Isolated Microgridscs.LG
Paper uses DRL for smart MG energy dispatch, improving stability and performance.
problem Improving energy dispatch in IoT-driven smart MGs with DGs, PVs, and batteries.
method Formulated POMDP model, proposed FH-DDPG and FH-RDPG algorithms, compared with baseline algorithms.
result Proposed algorithms enhance MG performance and stability under uncertainty.
Machine learning methods have shown promise in predicting molecular properties, and given sufficient training data machine learning approaches can enable rapid high-throughput virtual screening of large libraries of compounds. Graph-based neural network architectures have emerged in recent years as the most successful …
Paper benchmarks and customizes energy forecasting methods.
problem Energy forecasting challenges and differences from traditional time series.
method Collected large-scale load datasets and renewable energy datasets. Developed feature engineering and customized loss functions.
result Comprehensive evaluation of 21 forecasting methods in energy datasets.
An on-going debate in the energy economics and power market community has raised the question if energy-only power markets are increasingly failing due to growing feed-in shares from subsidized renewable energy sources (RES). The short answer to this is: No, they are not failing. Energy-based power markets are, however…
Organic Solar Cells are a promising technology for solving the clean energy crisis in the world. However, generating candidate chemical compounds for solar cells is a time-consuming process requiring thousands of hours of laboratory analysis. For a solar cell, the most important property is the power conversion efficie…
The paper analyzes MENA region's energy consumption and policy needs for renewable energy.
problem High dependency on oil and low renewable energy penetration in MENA region.
method Analysis of World Bank datasets and policy portfolio in MENA countries.
result MENA region has high potential for solar energy but faces challenges in decoupling economic growth from energy consumption.
The study improves solar irradiance forecasts for Chile using machine learning.
problem Accurate short-term PV power forecasts for Chile's Atacama Desert.
method 8-member ensemble forecasts of solar irradiance using WRF model, calibrated with EMOS and DRN.
result Machine learning-based post-processing methods improve forecast accuracy and calibration.
New method improves wind and solar energy forecasts by 48 hours.
problem Volatility of wind and solar energy makes accurate forecasts difficult.
method Two-step machine learning approach to calibrate ensemble forecasts.
result Statistical post-processing improves forecast skill by at least 48 hours.