The Sequence-to-Sequence (S2S) and Sequence-to-Point (S2P) optimization methods achieve remarkable accuracy results for load disaggregation tasks. Internally, they rely on neural networks, trained to identify the power consumption of a single appliance under consideration from a sequence of aggregate power data. Their most important configuration parameter - the number of input data samples to consider - is, however, mostly set to a fixed value. As a result thereof, the amount of historical data available at the algorithm's input is governed by the sampling interval of the used input data. For example, UK-DALE [5] provides samples every 6 s, so a sequence length of 599 samples (as proposed in [9]) makes approximately 1 h of historical data available to the disaggregation algorithm. No analyses of the impact of the sequence length on the NILM performance have been documented in literature to date. We hence present a methodological assessment of the sensitivity of S2S and S2P to variations of their input sequence length parameter. Our results show that setting a per-device parameter value leads to improved disaggregation results; however, the required values need to be determined empirically, as they are unrelated to the appliances' operational durations. Even if only a single value may be set, an informed choice (rather than using the default value) can drastically improve NILM performance.
[1]
Jack Kelly,et al.
The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes
,
2014,
Scientific Data.
[2]
Shaodan Ma,et al.
Non-intrusive Load Monitoring based on Convolutional Neural Network with Differential Input
,
2019,
Procedia CIRP.
[3]
R. Venkatesha Prasad,et al.
LocED: Location-aware Energy Disaggregation Framework
,
2015,
BuildSys@SenSys.
[4]
G. W. Hart,et al.
Nonintrusive appliance load monitoring
,
1992,
Proc. IEEE.
[5]
Paulo C. M. Meira,et al.
Towards reproducible state-of-the-art energy disaggregation
,
2019,
BuildSys@SenSys.
[6]
Haimonti Dutta,et al.
NILMTK: an open source toolkit for non-intrusive load monitoring
,
2014,
e-Energy.
[7]
Jack Kelly,et al.
Neural NILM: Deep Neural Networks Applied to Energy Disaggregation
,
2015,
BuildSys@SenSys.
[8]
Charles A. Sutton,et al.
Sequence-to-point learning with neural networks for nonintrusive load monitoring
,
2016,
AAAI.
[9]
Christoph Klemenjak,et al.
How does Load Disaggregation Performance Depend on Data Characteristics?: Insights from a Benchmarking Study
,
2020,
e-Energy.