TY - GEN
T1 - Uncertainty Estimation for Energy Consumption Nowcasting
AU - Rey-Arnal, Danel
AU - Laña, Ibai
AU - Bringas, Pablo G.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - In recent years nowcasting systems have been required to perform on more complex scenarios and to a better standard than ever. This work aims at studying the relation between the performance of an array of prediction models improved via data aggregation and the measurement of uncertainty and its potential shift as a consequence of such aggregation. In order to gauge the impact of this approach we propose an experimental framework in which the predictive capabilities of distinct modeling approaches in different aggregation circumstances, is assessed in conjunction with the study of model uncertainty measurements. The results show that the not only the aggregation level, but the modeling choice can have an impact in terms of uncertainty quantification, revealing different sizes of confidence intervals. These measurements represent a novel approach to the model and aggregation level selection process.
AB - In recent years nowcasting systems have been required to perform on more complex scenarios and to a better standard than ever. This work aims at studying the relation between the performance of an array of prediction models improved via data aggregation and the measurement of uncertainty and its potential shift as a consequence of such aggregation. In order to gauge the impact of this approach we propose an experimental framework in which the predictive capabilities of distinct modeling approaches in different aggregation circumstances, is assessed in conjunction with the study of model uncertainty measurements. The results show that the not only the aggregation level, but the modeling choice can have an impact in terms of uncertainty quantification, revealing different sizes of confidence intervals. These measurements represent a novel approach to the model and aggregation level selection process.
KW - Aggregation
KW - Nowcasting
KW - Uncertainty
UR - http://www.scopus.com/inward/record.url?scp=85203192433&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-68302-2_9
DO - 10.1007/978-3-031-68302-2_9
M3 - Conference contribution
AN - SCOPUS:85203192433
SN - 9783031683015
T3 - Communications in Computer and Information Science
SP - 102
EP - 114
BT - Database and Expert Systems Applications - DEXA 2024 Workshops - IWCFS, AISys, CIU, Proceedings
A2 - Moser, Bernhard
A2 - Fischer, Lukas
A2 - Glock, Anna-Christina
A2 - Mayr, Michael
A2 - Luftensteiner, Sabrina
A2 - Mashkoor, Atif
A2 - Sametinger, Johannes
PB - Springer Science and Business Media Deutschland GmbH
T2 - 9th International Workshop on Cyber-Security and Functional Safety in Cyber-Physical Systems, IWCFS 2024, 4th International Workshop on AI System Engineering: Math, Modelling and Software, AISys 2024, and 2nd International Workshop on Certainty in Uncertainty: Exploring Probabilistic Approaches in AI, CIU 2024 held at the 35th International Conference on Database and Expert Systems Applications, DEXA 2024
Y2 - 26 August 2024 through 28 August 2024
ER -