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Books on the topic 'SOLAR ENERGY FORECASTING'

Author: Grafiati

Published: 11 September 2023

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1

United States. Bureau of Labor Statistics, ed. Careers in solar power. Washington, D.C.]: U.S. Bureau of Labor Statistics, 2011.

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2

Investing in solar stocks: An investor's guide to winning in the global renewable energy market. New York: McGraw-Hill, 2009.

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3

Paulescu, Marius. Weather Modeling and Forecasting of PV Systems Operation. London: Springer London, 2013.

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4

National Renewable Energy Laboratory (U.S.) and IEEE Photovoltaic Specialists Conference (37th : 2011 : Seattle, Wash.), eds. An economic analysis of photovoltaics versus traditional energy sources: Where are we now and where might we be in the near future? : preprint. Golden, Colo.]: National Renewable Energy Laboratory, 2011.

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5

Solar Energy Technologies Program (U.S.), National Renewable Energy Laboratory (U.S.), and IEEE Photovoltaic Specialists Conference (37th : 2011 : Seattle, Wash.), eds. An economic analysis of photovoltaics versus traditional energy sources: Where are we now and where might we be in the near future? [Golden, Colo.]: National Renewable Energy Laboratory, U.S. Dept. of Energy, Office of Energy Efficienty and Renewable Energy, 2011.

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6

Lipták, Béla G. Post-oil energy technology: The world's first solar-hydrogen demonstration power plant. Boca Raton: CRC Press, 2009.

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7

European Commission. Directorate-General for Energy and European Photovoltaic Industry Association, eds. Photovoltaics in 2010. Luxembourg: Office for Official Publications of the European Communities, 1996.

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8

Liptak, Bela G. Post-oil energy technology: After the age of fossil fuels. Boca Raton, Fl: Taylor & Francis, 2008.

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9

Kleissl, Jan. Solar Energy Forecasting and Resource Assessment. Elsevier Science & Technology Books, 2013.

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10

Solar Energy Forecasting and Resource Assessment. Elsevier, 2013. http://dx.doi.org/10.1016/c2011-0-07022-9.

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11

Kleissl, Jan. Solar Energy Forecasting and Resource Assessment. Elsevier Science & Technology Books, 2013.

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12

Solar Radiation - Measurements, Modeling and Forecasting for Photovoltaic Solar Energy Applications [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.87671.

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13

Perez, Richard. Wind Field and Solar Radiation Characterization and Forecasting: A Numerical Approach for Complex Terrain. Springer, 2018.

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14

Paulescu, Marius, Eugenia Paulescu, and Paul Gravila. Weather Modeling and Forecasting of PV Systems Operation. Springer, 2012.

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15

Badescu, Viorel, Marius Paulescu, Eugenia Paulescu, and Paul Gravila. Weather Modeling and Forecasting of PV Systems Operation. Springer, 2012.

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16

Badescu, Viorel, Marius Paulescu, Eugenia Paulescu, and Paul Gravila. Weather Modeling and Forecasting of PV Systems Operation. Springer, 2014.

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17

Wiley, John. Photovoltaic Materials: An Analysis of Emerging Technology and Markets (Technical Insights, R-259). John Wiley & Sons Inc, 1999.

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18

Rogol, Michael. Explosive Growth. Live Oak Book Company, 2011.

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19

Alotaibi, Majed A., Hasmat Malik, and Amit Kumar Yadav. Intelligent Data Analytics for Solar Energy Prediction and Forecasting: Advances in Resource Assessment and PV Systems Optimization. Elsevier, 2024.

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20

Paddock, LeRoy, and Karyan San Martano. Energy Supply Planning in a Distributed Energy Resources World. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198822080.003.0021.

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Traditional electricity planning in the United States has typically been focused on forecasting future demand and identifying the need for new large generating facilities that involve one-way flows from central power plants to customers. However, legal innovation has incentivized the deployment of more energy resources at the customer end of the electricity network and technological innovation has facilitated rapid expansion in the number and capacity of these distributed energy resources. These changes now require innovation in the type of planning for future energy resources that must be employed to ensure an efficient and reliable electric energy network. Deployment of the rapidly growing number of distributed energy resources —solar and wind generation, combined heat and power, fuel cells, battery storage, demand response and energy efficiency—is facilitated by an increasingly smart grid. This chapter proposes a new, distributed resource-planning process to better integrate all of these distributed resources into the grid.

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21

Sotnyk, M. Power supply for educational institutions: efficiency and alternatives. Accent Graphics Communications & Publishing, 2020. http://dx.doi.org/10.29013/msotnyk.pseiea.2020.146.

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Proposed methodological approaches to modeling short-term forecasting and long-term planning of electrical consumption in educational institutions based on retrospective data. A logic-structural model and software of the circuit “object of monitoring of electric consumption — factors of influence — regulatory tools” of an automated system for controlling the efficiency of energy consumption in educational institutions have been developed. There are given practical recommendations of feasibility study of introduction of alternative power supply sources in educational institutions, in particular: solar generation, heat pumps, autonomous energy sources, etc. Proposed scientific and methodological approaches to the introduction of an organizational and economic mechanism for managing the development of renewable energy in educational institutions and a motivation system for employees of the energy management service. The monograph is a generalization of scientific research conducted by employees of Sumy State University during the state budget research work “Model of an efficiency management and forecasting system for the consumption of electric energy” (State Registration No. 0118U003583). The monograph is intended for researchers and specialists in the implementation of energy management systems

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22

Post-Oil Energy Technology: After the Age of Fossil Fuels. CRC, 2008.

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