Non-intrusive Load Monitoring based on Self-supervised Learning

Deep learning models for non-intrusive load monitoring (NILM) tend to require a large amount of labeled data for training. However, it is difficult to generalize the trained models to unseen sites due to different load characteristics and operating patterns of appliances between data sets. For addressing such problems, self-supervised learning (SSL) is proposed in this paper, where labeled appliance-level data from the target data set or house is not required. Initially, only the aggregate power readings from target data set are required to pre-train a general network via a self-supervised pretext task to map aggregate power sequences to derived representatives. Then, supervised downstream tasks are carried out for each appliance category to fine-tune the pre-trained network, where the features learned in the pretext task are transferred. Utilizing labeled source data sets enables the downstream tasks to learn how each load is disaggregated, by mapping the aggregate to labels. Finally, the fine-tuned network is applied to load disaggregation for the target sites. For validation, multiple experimental cases are designed based on three publicly accessible REDD, UK-DALE, and REFIT data sets. Besides, state-of-the-art neural networks are employed to perform NILM task in the experiments. Based on the NILM results in various cases, SSL generally outperforms zero-shot learning in improving load disaggregation performance without any sub-metering data from the target data sets.Comment: 12 pages,10 figure

Multi-timescale Event Detection in Nonintrusive Load Monitoring based on MDL Principle

Load event detection is the fundamental step for the event-based non-intrusive load monitoring (NILM). However, existing event detection methods with fixed parameters may fail in coping with the inherent multi-timescale characteristics of events and their event detection accuracy is easily affected by the load fluctuation. In this regard, this paper extends our previously designed two-stage event detection framework, and proposes a novel multi-timescale event detection method based on the principle of minimum description length (MDL). Following the completion of step-like event detection in the first stage, a long-transient event detection scheme with variable-length sliding window is designed for the second stage, which is intended to provide the observation and characterization of the same event at different time scales. In that, the context information in the aggregated load data is mined by motif discovery, and then based on the MDL principle, the proper observation scales are selected for different events and the corresponding detection results are determined. In the post-processing step, a load fluctuation location method based on voice activity detection (VAD) is proposed to identify and remove the unreasonable events caused by fluctuations. Based on newly proposed evaluation metrics, the comparison tests on public and private datasets demonstrate that our method achieves higher detection accuracy and integrity for events of various appliances across different scenarios.Comment: 11 pages,16 figure

Apply Graph Signal Processing on NILM: An Unsupervised Approach Featuring Power Sequences

As a low-cost demand-side management application, non-intrusive load monitoring (NILM) offers feedback on appliance-level electricity usage without extra sensors. NILM is defined as disaggregating loads only from aggregate power measurements through analytical tools. Although low-rate NILM tasks have been conducted by unsupervised approaches based on graph signal processing (GSP) concepts, enhancing feature selection can still contribute to performance improvement. Therefore, a novel unsupervised GSP-based NILM approach with power sequence feature (STS-UGSP) is proposed in this paper. First, state transition sequences (STS) are extracted from power readings and featured in clustering and matching, instead of power changes and steady-state power sequences featured in other GSP-based NILM works. When generating graph in clustering, dynamic time warping distances between STSs are calculated for similarity quantification. After clustering, a forward-backward power STS matching algorithm is proposed for searching each STS pair of an operational cycle, utilizing both power and time information. Finally, load disaggregation results are obtained based on STS clustering and matching results. STS-UGSP is validated on three publicly accessible datasets from various regions, generally outperforming four benchmarks in two evaluation metrics. Besides, STS-UGSP estimates closer energy consumption of appliances to the ground truth than benchmarks

Study on business models of distributed generation in China

Along with the implementation of electricity sales-side reform and incremental power distribution investment liberalization under China’s new round of electricity market reform, the main subjects of distributed generation investment, construction, and operations have become more various, and the approaches as well as the ways that distributed generation takes part in the market have been more flexible. How to operate distributed generation economically and efficiently in this market environment has become an urgent issue to be addressed. Based on the domestic development situation of China’s distributed generation, this paper introduces typical existing business models of distributed generation, and elaborates the correlation between electricity market reform and business models of distributed generation in depth in combination with the contents of electricity market reform. At last, several business models of distributed generation which are feasible to be implemented in China have been proposed under the new electricity market reform based on successful international distributed generation operation experiences in the terms of stimulating stakeholders’ participation in the investment and operations of distributed generation. Characteristics of these recommended models are compared and analyzed as well. Keywords: Distributed generation, Electricity market reform, Business model

Synthesis of Half-Titanocene Complexes Containing π,π-Stacked Aryloxide Ligands, and Their Use as Catalysts for Ethylene (Co)polymerizations

A family of half-titanocene complexes bearing π,π-stacked aryloxide ligands and their catalytic performances towards ethylene homo-/co- polymerizations were disclosed herein. All the complexes were well characterized, and the intermolecular π,π-stacking interactions could be clearly identified from single crystal X-ray analysis, in which a stronger interaction could be reflected for aryloxides bearing bigger π-systems, e.g., pyrenoxide. Due to the formation of such interactions, these complexes were able to highly catalyze the ethylene homopolymerizations and copolymerization with 1-hexene comonomer, even without any additiveson the aryloxide group, which showed striking contrast to other half-titanocene analogues, implying the positive influence of π,π-stacking interaction in enhancing the catalytic performances of the corresponding catalysts. Moreover, it was found that addition of external pyrene molecules was capable of boosting the catalytic efficiency significantly, due to the formation of a stronger π,π-stacking interaction between the complexes and pyrene molecules