Expanding wind and solar contributions to our energy supply could be an important means for reducing GHG emissions, since wind and solar energy resources are very large. In the U.S. wind resources have been estimated to be large enough to supply more than 16 times current total electricity supply. Similarly large estimates have been made for the potential of solar energy.[i]
Yet the current contribution to energy supply from wind and solar energy in the U.S. are surprisingly small; wind accounts for less than 5% of domestic electricity generation, and solar contributes less than 1%. Far larger amounts of wind and solar power could be accommodated on utility grids, without the need for grid-scale storage to address the intermittency of the resources.[ii]
Considering the climate-related benefits of wind and solar electricity, a public that is more aware and better-informed about the potential for wind and solar energy might improve the possibilities for more accelerated deployment of wind and solar generation.[iii] One approach for raising awareness is providing stakeholders, including the general public, policy makers, and others, with simple tools they can use to see daily forecasts of the potential for wind and solar power in localities of interest to them. There is a rich literature describing sophisticated methodologies for wind[iv],[v],[vi],[vii],[viii],[ix] and solar[v],[vi],[x],[xi],[xii] power forecasting. Moreover, sophisticated commercial products are offered by companies for forecasting wind[xiii] and solar[xiv]power production. But sophisticated models and sophisticated commercial products are of limited utility for the non-commercial purposes of informing public discussions and raising broader awareness of wind and solar energy. Simple, freely available tools for such purposes that can generate localized daily forecasts of solar and wind electricity generation potential are desirable.
To help meet this need, Climate Central is developing web-based user-friendly solar and wind electricity forecasters for the U.S. In addition to showing geographically-specific forecasts of the amount of wind or solar electricity that will be produced tomorrow, the next day, or the day after next, the tools will also offer relevant background context and comparisons to help put in perspective the forecast amounts. Climate Central’s wind electricity forecasting tool will be discussed in this talk.
[i] Turkenburg, W. (Convening Lead Author), et al., “Renewable Energy,” chapter 11 in The Global Energy Assessment, Cambridge University Press, 2012.
[ii] Lew, D., et al., “The Western Wind and Solar Integration Study Phase 2,” NREL/TP-5500-55588, National Renewable Energy Laboratory, Golden, CO, September 2013.
[iii] Kandpal, T.C. and Broman, L., “Renewable energy education: a global status review,” Renewable and Sustainable Energy Reviews, 34: 300-324, 2014.
[iv] Liu, Q., Miao, Q, Liu, J.J., and Yang, W., “Solar and wind energy resources and prediction,” Journal of Renewable and Sustainable Energy, 1: 43105, 2009.
[v] Zheng, Z.W., Chen, Y.Y., Huo, M.M., and Zhao, B., “An overview: the development of prediction technology of wind and photovoltaic power generation,” Energy Procedia, 12: 601-608, 2011.
[vi] Vladislavleva, E., Friedrich, T., Neumann, F., and Wagner, M., “Predicting the energy output of wind farms based on weather data: important variables and their correlation,” Renewable Energy, 50: 236-243, 2013.
[vii] Colak, I., Sagiroglu, S., and Yeilbudak, M., “Data mining and wind power prediction: a literature review,” Renewable Energy, 46: 241-247, 2012.
[viii] Wu, B., Song, M., Chen, K., He, Z, and Zhang, X., “Wind power prediction system for wind farm based on auto regressive statistical model and physical model,” Journal of Renewable and Sustainable Energy, 6: 013101, 2014.
[ix] Zhu, B., Chen, M., Wade, N., and Ran, L. “A prediction model for wind farm power generation base on fuzzy modeling,” Procedia Environmental Sciences, 12: 122-129, 2012.
[x] Fernandez-Jimeniz, L., et al., “Short-term power forecasting system for photovoltaic plants,” Renewable Energy, 44: 311-317, 2012.
[xi] Zamo, M., Mestre, O., Arbogast, P., and Pannekoucke, O., “A benchmark of statistical regression methods for short-term forecasting of photovoltaic electricity production, part I: deterministic forecast of hourly production,” Solar Energy, 105: 792-803, 2014.
[xii] Izgi, E., Oztopal, A., Yerli, B, Kaymak, M.K., and Sahin, A.D., “Short—mid-term solar power prediction by using artificial neural networks,” Solar Energy, 86: 725-733, 2012.
[xiii] Commercial wind forecasting tools available include those offered by 3Tier and AWS Truepower.
[xiv] Commercial solar forecasting tools available include those offered by 3Tier and AWS Truepower.
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