TY - GEN
T1 - Individual household modeling of photovoltaic adoption
AU - Letchford, Joshua
AU - Lakkaraju, Kiran
AU - Vorobeychik, Yevgeniy
N1 - Publisher Copyright:
Copyright © 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2014
Y1 - 2014
N2 - We consider the question of predicting solar adoption using demographic, economic, peer effect and predicted system characteristic features. We use data from San Diego county to evaluate both discrete and continuous models. Additionally, we consider three types of sensitivity analysis to identify which features seem to have the greatest effect on prediction accuracy.
AB - We consider the question of predicting solar adoption using demographic, economic, peer effect and predicted system characteristic features. We use data from San Diego county to evaluate both discrete and continuous models. Additionally, we consider three types of sensitivity analysis to identify which features seem to have the greatest effect on prediction accuracy.
UR - https://www.scopus.com/pages/publications/84940385096
M3 - Conference contribution
AN - SCOPUS:84940385096
T3 - AAAI Fall Symposium - Technical Report
SP - 19
EP - 23
BT - Energy Market Prediction - Papers from the AAAI Fall Symposium, Technical Report
PB - AI Access Foundation
T2 - 2014 AAAI Fall Symposium
Y2 - 13 November 2014 through 15 November 2014
ER -