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Your search keyword '"Boland, John"' showing total 621 results
Start Over Author "Boland, John" Publication Type Academic Journals
621 results on '"Boland, John"'

1. Understanding how junction resistances impact the conduction mechanism in nano-networks

Author

Gabbett, Cian, Kelly, Adam G., Coleman, Emmet, Doolan, Luke, Carey, Tian, Synnatschke, Kevin, Liu, Shixin, Dawson, Anthony, O’Suilleabhain, Domhnall, Munuera, Jose, Caffrey, Eoin, Boland, John B., Sofer, Zdeněk, Ghosh, Goutam, Kinge, Sachin, Siebbeles, Laurens D. A., Yadav, Neelam, Vij, Jagdish K., Aslam, Muhammad Awais, Matkovic, Aleksandar, and Coleman, Jonathan N.

Published
2024
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16. Verification of deterministic solar forecasts

Author

Yang, Dazhi, Alessandrini, Stefano, Antonanzas, Javier, Antonanzas-Torres, Fernando, Badescu, Viorel, Beyer, Hans Georg, Blaga, Robert, Boland, John, Bright, Jamie M, Coimbra, Carlos FM, David, Mathieu, Frimane, Âzeddine, Gueymard, Christian A, Hong, Tao, Kay, Merlinde J, Killinger, Sven, Kleissl, Jan, Lauret, Philippe, Lorenz, Elke, van der Meer, Dennis, Paulescu, Marius, Perez, Richard, Perpiñán-Lamigueiro, Oscar, Peters, Ian Marius, Reikard, Gordon, Renné, David, Saint-Drenan, Yves-Marie, Shuai, Yong, Urraca, Ruben, Verbois, Hadrien, Vignola, Frank, Voyant, Cyril, and Zhang, Jie

Subjects
Affordable and Clean Energy, Solar forecasting, Measure-oriented forecast verification, Distribution-oriented forecast verification, Skill score, Combination of climatology and persistence, Engineering, Built Environment and Design, Energy
Abstract

The field of energy forecasting has attracted many researchers from different fields (e.g., meteorology, data sciences, mechanical or electrical engineering) over the last decade. Solar forecasting is a fast-growing subdomain of energy forecasting. Despite several previous attempts, the methods and measures used for verification of deterministic (also known as single-valued or point) solar forecasts are still far from being standardized, making forecast analysis and comparison difficult. To analyze and compare solar forecasts, the well-established Murphy–Winkler framework for distribution-oriented forecast verification is recommended as a standard practice. This framework examines aspects of forecast quality, such as reliability, resolution, association, or discrimination, and analyzes the joint distribution of forecasts and observations, which contains all time-independent information relevant to verification. To verify forecasts, one can use any graphical display or mathematical/statistical measure to provide insights and summarize the aspects of forecast quality. The majority of graphical methods and accuracy measures known to solar forecasters are specific methods under this general framework. Additionally, measuring the overall skillfulness of forecasters is also of general interest. The use of the root mean square error (RMSE) skill score based on the optimal convex combination of climatology and persistence methods is highly recommended. By standardizing the accuracy measure and reference forecasting method, the RMSE skill score allows—with appropriate caveats—comparison of forecasts made using different models, across different locations and time periods.

Published
2020

24. A Standardized Sky Condition Classification Method for Multiple Timescales and Its Applications in the Solar Industry.

Author

Poddar, Shukla, Kay, Merlinde, and Boland, John

Subjects
SOLAR energy industries, POWER resources, SOLAR energy, ENERGY consumption, RENEWABLE energy sources
Abstract

The deployment of photovoltaic (PV) systems has increased globally to meet renewable energy targets. Intermittent PV power generated due to cloud-induced variability introduces reliability and grid stability issues at higher penetration levels. Variability in power generation can induce voltage fluctuations within the distribution system and cause adverse effects on power quality. Therefore, it is essential to quantify resource variability to mitigate an intermittent power supply. In this study, we propose a new scheme to classify the sky conditions that are based on two common variability metrices: daily clear-sky index and normalized aggregate ramp rates. The daily clear-sky index estimates the cloudiness in the sky, and ramp rates account for the variability introduced in the system generation due to sudden cloud movements. This classification scheme can identify clear-sky, highly variable, low intermittent, high intermittent and overcast days. By performing a Chi-square test on the training and test sets, we obtain Chi-square statistic values greater than 3 with p-value > 0.05. This indicates that the distribution of the training and test clusters are similar, indicating the robustness of the proposed sky classification scheme. We have demonstrated the applicability of the scheme with diverse datasets to show that the proposed classification scheme can be homogenously applied to any dataset globally despite their temporal resolution. Using various case studies, we demonstrate the potential applications of the scheme for understanding resource allocation, site selection, estimating future intermittency due to climate change, and cloud enhancement effects. The proposed sky classification scheme enhances the precision and reliability of solar energy forecasts, optimizing system performance and maximizing energy production efficiency. This improved accuracy is crucial for variability control and planning, ensuring optimal output from PV plants. [ABSTRACT FROM AUTHOR]

Published
2024
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32. Constructing Interval Forecasts for Solar and Wind Energy Using Quantile Regression, ARCH and Exponential Smoothing Methods.

Author

Boland, John

Subjects
*STATISTICAL smoothing, *WIND power, *SOLAR energy, *WIND forecasting, *QUANTILE regression, *ENERGY consumption
Abstract

The research reported in this article focuses on a comparison of two different approaches to setting error bounds, or prediction intervals, on short-term forecasting of solar irradiation as well as solar and wind farm output. Short term in this instance relates to the time scales applicable in the Australian National Electricity Market (NEM), which operates on a five-minute basis throughout the year. The Australian Energy Market Operator (AEMO) has decided in recent years that, as well as point forecasts of energy, it is advantageous for planning purposes to have error bounds on those forecasts. We use quantile regression as one of the techniques to construct the bounds. This procedure is compared to a method of forecasting the conditional variance by use of either ARCH/GARCH or exponential smoothing, whichever is more appropriate for the specific application. The noise terms for these techniques must undergo a normalising transformation before their application. It seems that, for certain applications, quantile regression performs better, and the other technique for some other applications. [ABSTRACT FROM AUTHOR]

Published
2024
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