Abstract
New methods are proposed for adjusting probabilistic forecasts to ensure coherence with the aggregation constraints inherent in temporal hierarchies. The different approaches nested within this framework include methods that exploit information at all levels of the hierarchy as well as a novel method based on cross-validation. The methods are evaluated using real data from two wind farms in Crete and electric load in Boston. For these applications, optimal decisions related to grid operations and bidding strategies are based on coherent probabilistic forecasts of energy power. Empirical evidence is also presented showing that probabilistic forecast reconciliation improves the accuracy of the probabilistic forecasts.
| Original language | English |
|---|---|
| Pages (from-to) | 364-379 |
| Number of pages | 16 |
| Journal | European Journal of Operational Research |
| Volume | 279 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - Dec 2019 |
Keywords
- Aggregation
- Cross-validation
- Forecasting
- Renewable energy generation
- Temporal hierarchies
Cite this
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Probabilistic forecast reconciliation with applications to wind power and electric load. / Jeon, Jooyoung; Panagiotelis, Anastasios; Petropoulos, Fotios.
In: European Journal of Operational Research, Vol. 279, No. 2, 12.2019, p. 364-379.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - Probabilistic forecast reconciliation with applications to wind power and electric load
AU - Jeon, Jooyoung
AU - Panagiotelis, Anastasios
AU - Petropoulos, Fotios
PY - 2019/12
Y1 - 2019/12
N2 - New methods are proposed for adjusting probabilistic forecasts to ensure coherence with the aggregation constraints inherent in temporal hierarchies. The different approaches nested within this framework include methods that exploit information at all levels of the hierarchy as well as a novel method based on cross-validation. The methods are evaluated using real data from two wind farms in Crete and electric load in Boston. For these applications, optimal decisions related to grid operations and bidding strategies are based on coherent probabilistic forecasts of energy power. Empirical evidence is also presented showing that probabilistic forecast reconciliation improves the accuracy of the probabilistic forecasts.
AB - New methods are proposed for adjusting probabilistic forecasts to ensure coherence with the aggregation constraints inherent in temporal hierarchies. The different approaches nested within this framework include methods that exploit information at all levels of the hierarchy as well as a novel method based on cross-validation. The methods are evaluated using real data from two wind farms in Crete and electric load in Boston. For these applications, optimal decisions related to grid operations and bidding strategies are based on coherent probabilistic forecasts of energy power. Empirical evidence is also presented showing that probabilistic forecast reconciliation improves the accuracy of the probabilistic forecasts.
KW - Aggregation
KW - Cross-validation
KW - Forecasting
KW - Renewable energy generation
KW - Temporal hierarchies
UR - http://www.scopus.com/inward/record.url?scp=85066323584&partnerID=8YFLogxK
U2 - 10.1016/j.ejor.2019.05.020
DO - 10.1016/j.ejor.2019.05.020
M3 - Article
VL - 279
SP - 364
EP - 379
JO - European Journal of Operational Research
JF - European Journal of Operational Research
SN - 0377-2217
IS - 2
ER -