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This work is supported by the US Department of Energy, the Los Angeles Department of Water and Power, and the NaAonal Science FoundaAon. The views of authors expressed herein do not necessarily reflect those of the sponsors.
Performance Measures for Electricity Consumption Prediction
Saima Aman (Advisors: Yogesh Simmhan and Viktor K. Prasanna)
http://ganges.usc.edu
• risk-‐adjusted improvement over baseline • factor in vola6lity of model with respect to baseline
PredicAon Bias
Scale Independence
Reliability
Cost
VolaAlity
• understand the frequency of over-‐ or under-‐predic6on • under-‐predic6on might miss the peak
• compare across different scales (unlike MAE, RMSE) • address diversity in customers
• how oEen the model performs beFer than a baseline or within an error threshold
• quan6fy the cost of collec6ng data, training and applying a model for predic6on
Domain Bias Percentage Error (DBPE) An asymmetric loss funcAon is used to assign different costs to over and under predicAons. These costs are applicaAon-‐specific. (Reduces to MAPE when costs are same)
Problem: EvaluaAon of KWh predicAon
Coefficient of Varia:on of RMSE (CV-‐RMSE) The root mean square error is divided by the mean of observed values. The normalized RMSE can then be used to compare across scales.
Reliability, REL Measures the count of performances less than the error threshold.
Rela:ve Improvement, RIM Measures the count of performances beVer than the baseline.
Need for novel Performance Measures
Data Cost, DC The number of unique values of all features in the model. Compute Cost, CC The Ame in seconds required to train a model Normalized Model cost, C = f(DC, CC)/m
Cost-‐Benefit Metric, CBM Measures the relaAve benefit of using a model with respect to normalized cost.
Vola:lity Adjusted Benefit-‐ BVM. It is adapted from the Sharpe raAo used in the finance domain for measuring benefit to risk raAo.
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
Annual Mean DoW Mean DoY Mean TS 1 wk ahead TS 2 wk ahead Ts 3 wk ahead TS 4-‐wk ahead RT
CVRMSE DBPE (0.5, 1.5)
Ini:al Results
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
TS 1 wk ahead
TS 2 wk ahead
Ts 3 wk ahead TS 4-‐wk ahead
RT
RIM
REL
BVM
Mo:va:on: Dynamic Demand Response (D2R)
Dynamic decision making for • start Ame • duraAon • depth (kWh) • customer selecAon • curtailment strategy selecAon
Error Measures (smaller is beVer) Goodness Measures (larger is beVer)
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