Authors:
Marc Wenninger
1
;
Jochen Schmidt
1
and
Toni Goeller
2
Affiliations:
1
Rosenheim University of Applied Sciences, Germany
;
2
MINcom GmbH, Germany
Keyword(s):
Real Time Pricing (RTP), Household Appliance Usage Prediction, Demand Side Management.
Related
Ontology
Subjects/Areas/Topics:
Energy and Economy
;
Energy-Aware Systems and Technologies
;
Load Balancing in Smart Grids
;
Optimization Techniques for Efficient Energy Consumption
;
Smart Grids
Abstract:
Shifting energy peak load is a subject that plays a huge role in the currently changing energy market, where
renewable energy sources no longer produce the exact amount of energy demanded. Matching demand to supply
requires behavior changes on the customer side, which can be achieved by incentives such as Real-Time-Pricing
(RTP). Various studies show that such incentives cannot be utilized without a complexity reduction, e. g., by
smart home automation systems that inform the customer about possible savings or automatically schedule
appliances to off-peak load phases. We propose a probabilistic appliance usage prediction based on historical
energy data that can be used to identify the times of day where an appliance will be used and therefore make
load shift recommendations that suite the customer’s usage profile.
A huge issue is how to provide a valid performance evaluation for this particular problem. We will argue why
the commonly used accuracy metric is not suitable, a
nd suggest to use other metrics like the area under the
Receiver Operating Characteristic (ROC) curve, Matthews Correlation Coefficient (MCC) or F 1 -Score instead.
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