@article{35939,
  author = {Jessica Granderson and Samuel Fernandes and Eliot Crowe and Mrinalini Sharma and David A Jump and Devan Johnson},
  title = {Accuracy of hourly energy predictions for demand flexibility applications},
  abstract = {<p>Decarbonization goals in the United States electricity sector are increasing the levels of renewable energy generation<br />in the electricity supply system, and are driving increased attention to building electrification, which will increase<br />the magnitude and shift the timing of the electricity system peak. These changes are motivating new approaches to<br />coordinate building electricity demand with low-carbon renewable generation, elevating the importance of demand<br />flexibility (DF) in buildings and the need to quantify the temporal impacts of DF. In this paper, we first characterize<br />the hourly predictive accuracy of six commonly used baseline models in an application context of quantifying<br />building-level load shift. Our analysis revealed insights such as hours of the day (afternoons), periods of the week<br />(weekends), and seasons (summer) that were predicted with more accuracy than other time periods. In addition, the<br />analysis showed tendencies toward overprediction or underprediction of load. Secondly, we provide the first<br />published investigation of baseline erosion from repeated dispatch of building load shifting. We observed that as the<br />baseline period is pushed back further from the prediction day, the distribution of errors across baseline model<br />predictions increases, with notable inflection points near the three-week erosion point for two of the three models.</p>},
  year = {2023},
  journal = {Energy and Buildings},
  volume = {Vol.295 No..113297},
  month = {06/2023},
  doi = {https://doi.org/10.1016/j.enbuild.2023.113297},
  language = {eng},
}