Journal
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
Volume 66, Issue 11, Pages 1900-1904Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSII.2019.2891704
Keywords
Forecasting; Aggregates; Circuits and systems; Monitoring; Eigenvalues and eigenfunctions; Power grids; Power demand; Power forecasting; load disaggregation; non-intrusive load monitoring (NILM); spectral clustering; smart grid
Categories
Funding
- IC-IMPACTS
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Forecasting energy or power usage is an important part of providing a stable supply of power to all customers on a power grid. We present a novel method that aims to forecast the power consumption of a single house, or a set of houses, based on non-intrusive load monitoring (NILM) and graph spectral clustering. In the proposed method, the aggregate power signal is decomposed into individual appliance signals and each appliance's power is forecasted separately. Then the total power forecast is formed by aggregating forecasted power levels of individual appliances. We use four publicly available datasets (reference energy disaggregation dataset, rainforest automation energy, almanac of minutely power dataset version 2, tracebase) to test our forecasting method and report its accuracy. The results show that our method is more accurate compared to popular existing approaches, such as autoregressive integrated moving average, similar profile load forecast, artificial neural network, and recent NILM-based forecasting.
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