Residential Power Forecasting Using Load Identification and Graph Spectral Clustering

Last week I found out that a journal paper I helped co-author with colleagues Chinthaka Dinesh and Ivan V. Bajić was accepted for publication. Titled Residential Power Forecasting Using Load Identification and Graph Spectral Clustering it will be published in journal IEEE Transactions on Circuits and Systems II: Express Briefs. Here is the paper abstract:

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 (REDD, RAE, AMPds2, 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 (ARIMA), similar profile load forecast (SPLE), artificial neural network (ANN), and recent NILM-based forecasting.

Keywords: power forecasting, load disaggregation, non- intrusive load monitoring (NILM), spectral clustering, smart grid

Source code for this project will soon be available on GitHub.

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