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Capacity Factor (%) 24 22 20 18 16 14 12 10 8 makes it possible for long-term load forecasters to factor Btm pvs into their forecasts, where and when necessary. Concluding Remarks 1 2 3 4 5 6 7 Month 8 9 10 11 12 figure 10. The monthly average capacity factors in Massachusetts, 1998-2014. The upper and lower ends of each box represent the first and third quartiles, respectively. The band inside the box is the median value, and the ends of the lines extending above and below the boxes ("whiskers") reflect the degree of variability outside of the upper and lower quartiles. The red circles reflect statistical outliers. figure 9 illustrates the simulated per-unit Btm pv profile for the six New england states on 1-2 July 2007. these profiles reflect an arithmetic mean of all town profiles within each state and show the overall effect of weather in each state on the simulated Btm pv. Based on the same set of results, it is also possible to develop aggregate Btm pv profiles that reflect a specified town-level geographical distribution of installed Btm pvs by simply weighting the town profiles by each town's installed capacity. table 4 summarizes the average annual capacity factors for the six states over the entire 17-year NsrDB-based simulation period, and figure 10 is a box plot showing the variability of Btm pv's monthly capacity factors in massachusetts over the entire period. since New england typically experiences snow events during winter and the effects of snow cover were not explicitly modeled in the simulations, the annual performance metrics are somewhat optimistic for the winter months (and annually as a result). however, the winter results are representative of the solar resource availability in winter, absent snow. overall, the broad agreement between the granular, townlevel simulation results and measured data demonstrates that the Btm pv fleet simulation yielded reasonable, realistic results and highlights that Nrel's new NsrDB data set is not only spatially and temporally comprehensive but also of a very high quality. Given that the simulation period is 17 years, a relatively robust understanding of the patterns of solar resource availability is now possible in the area covered by NsrDB. perhaps, more importantly, coupling the results with longterm forecasts of Btm pv and historical loads enables a thorough investigation of net load patterns as Btm pv penetrations increase. using this information, greater clarity is now possible concerning the timing and magnitude of mitigation measures that may be required to integrate increasingly larger Btm pv penetrations in the future. lastly, the NsrDB now may/june 2018 more than 70 years ago, researchers recognized temperature as a driving factor of electricity demand. since then, temperature data has been widely used in load forecasting models. Due to the increasing need of accurate energy forecasts, more weather data are being adopted by the power industry. in this article we presented two utility applications that rely on weather data. We used some conventional and easily accessible weather data to offer a novel and probabilistic view of saiDi, which helps reveal the utility's reliability trend. in the Btm pv simulation study, we used some recently developed comprehensive weather data to tackle an emerging problem in renewable integration. researchers and practitioners have created a wealth of knowledge in the science of meteorology and atmosphere science in general. We hope that this article will inspire you to try out more weather data and expand the footprints of meteorology in the energy analytics arena. For Further Reading IEEE Guide for Electric Power Distribution Reliability Indices, ieee standard 1366-2012 (revision of ieee standard 13662003), may 2012. t. hong, J. Wilson, and J. Xie, "long term probabilistic load forecasting and normalization with hourly information," IEEE Trans. Smart Grid, vol. 5, no. 1, pp. 456-462, Jan. 2014. t. hong and s. fan, "probabilistic electric load forecasting: a tutorial review," Int. J. Forecasting, vol. 32, no. 3, pp. 914-938, July-sept. 2016. National solar radiation database. (2017). National renewable energy laboratory. [online]. available: https://nsrdb.nrel .gov/current-version#psm National renewable energy laboratory. system advisor model. [online]. available: https://sam.nrel.gov/ e. lorenz, t. scheidsteger, J. hurka, D. heinemann, and c. Kurz, "regional pv power prediction for improved grid integration," Progress Photovoltaics Res. Applicat., vol. 19, no. 7, pp. 757-771, Nov. 2011. Biographies Jonathan Black is with iso New england, holyoke, massachusetts. Alex Hofmann is with the american public power authority, arlington, virginia. Tao Hong is with the university of North carolina at charlotte. Joseph Roberts is with iso New england, holyoke, massachusetts. Pu Wang is with the sas institute inc., cary, North carolina. p&e ieee power & energy magazine 53