Disaggregation of Domestic Smart Meter Energy Data

University of London Imperial College of Science, Technology and Medicine Department of Computing Disaggregation of Domestic Smart Meter Energy Data Jack (Daniel) Kelly Submitted in part fulfilment of the requirements for the degree of Doctor of Philosophy in Computing of the University of London and the Diploma of Imperial College, April 2017 Abstract Many countries are rolling out smart electricity meters. A smart meter measures the aggregate energy consumption of an entire building. However, appliance-by-appliance energy consumption information may be more valuable than aggregate data for a variety of uses including reducing energy demand and improving load forecasting for the electricity grid. Electricity disaggregation algorithms – the focus of this thesis – estimate appliance-by-appliance electricity demand from aggregate electricity demand. This thesis has three main goals: 1) to critically evaluate the benefits of energy disaggregation; 2) to develop tools to enable rigorous disaggregation research; 3) to advance the state of the art in disaggregation algorithms. The first part of this thesis explores whether disaggregated energy feedback helps domestic users to reduce energy consumption; and discusses threats to the NILM. Evidence is collected, summarised and aggregated by means of a critical, systematic review of the literature. Multi- ple uses for disaggregated data are discussed. Our review finds no robust evidence to support the hypothesis that current forms of disaggregated energy feedback are more effective than aggregate energy feedback at reducing energy consumption in the general population. But the absence of evidence does not necessarily imply the absence of any beneficial effect of disaggregated feedback. The review ends with a discussion of ways in which the effectiveness of disaggregated feedback may be increased and a discussion of opportunities for new research into the effectiveness of disaggregated feedback. We conclude that more social science research into the effects of disaggregated energy feedback is required. This motivates the remainder of the thesis: to enable cost-effective research into the effects of disaggregated feedback, we work towards developing robust NILM algorithms and software. The second part of this thesis describes three tools and one dataset developed to enable disaggregation research. The first of these tools is a novel, low-cost data collection system, which records appliance-by-appliance electricity demand every six seconds and records the whole- home voltage and current at 16 kHz. This system enabled us to collect the UK’s first and only high-frequency (kHz) electricity dataset, the UK Disaggregated Appliance-Level Electric- ity dataset (UK-DALE). Next, to help the disaggregation community to conduct open, rigorous, repeatable research, we collaborated with other researchers to build the first open-source dis- i aggregation framework, NILMTK. NILMTK has gained significant traction in the community, both in terms of contributed code and in terms of users. The third tool described in this thesis is a metadata schema for disaggregated energy data. This schema was developed to make it easier for researchers to describe their own datasets and to reduce the effort required to import datasets. The third part of this thesis describes our effort to advance the state of the art in disaggregation algorithms. Three disaggregation approaches based on deep learning are discussed: 1) a form of recurrent neural network called ‘long short-term memory’ (LSTM); 2) denoising autoencoders; and 3) a neural network which regresses the start time, end time and average power demand of each appliance activation. The disaggregation performance was measured using seven metrics and compared to two ‘benchmark’ algorithms from NILMTK: combinatorial optimisation and factorial hidden Markov models. To explore how well the algorithms generalise to unseen houses, the performance of the algorithms was measured in two separate scenarios: one using test data from a house not seen during training and a second scenario using test data from houses which were seen during training. All three neural nets achieve better F1 scores (averaged over all five appliances) than either benchmark algorithm. The neural net algorithms also generalise well to unseen houses. ii Copyright The copyright of this thesis rests with the author and is made available under a Creative Commons Attribution Non-Commercial No Derivatives licence. Researchers are free to copy, distribute or transmit the thesis on the condition that they attribute it, that they do not use it for commercial purposes and that they do not alter, transform or build upon it. For any reuse or redistribution, researchers must make clear to others the licence terms of this work. iii iv Acknowledgements I would like to thank the following people: • My supervisor, Professor William Knottenbelt, who has been the perfect supervisor for me. Will gave me the freedom and the trust to explore new and interesting research directions, while providing frequent encouragement and guidance. Will also led by exam- ple: he is a shining light of honesty, warmth and sharing; both in the context of research and in the context of teaching. I have been very lucky to have had such a wonderful supervisor. • My second supervisor, Professor Andrew Davison, for first introducing me to the joys of machine learning when I took his Robotics course during my Computing MSc, and for his insight and encouragement during my PhD. • My collaborators on NILMTK and other projects, Dr Oliver Parson (who was at the University of Southampton and is now at Centrica Connected Home) and Nipun Batra (at IIIT Delhi). I thoroughly enjoyed our collaboration, learnt a lot, and met two of the most friendly and ambitious researchers in the community! • Professor Mario Bergés (CMU) for his encouragement, generosity and insight. • Professor Murray Shanahan for allowing me to co-supervise some highly inspiring and exciting MSc projects on the intersection between neuroscience and machine learning. • Pete Swabey for providing many fascinating (and sceptical) links on smart homes. • Dr Gareth Jones for his feedback on my draft thesis. • Dr Carrie Armel for engaging with me in email discussions about one of her papers, Armel et al. 2013. • Becky Sokoloski and Professor Wesley Schultz for email discussions about Becky’s masters thesis, Sokoloski 2015. • The Computing MSc students who worked on group projects and individual projects that I proposed. Co-supervising MSc projects was one of the most enjoyable and rewarding v activities during my PhD and I was very lucky to work with such motivated and smart people; many of whom have gone on to do great things. • Members of the community who have contributed code and comments to NILMTK and NILM Metadata. • The EPSRC for funding my PhD DTA. • Intel for providing a generous Fellowship throughout my PhD. • The Department of Computing’s Support Group (CSG) for their rapid support and un- wavering patience. I would especially like to thank Dr Lloyd Kamara, Mr Geoff Bruce and Mr Duncan White. • Dr Amani El-Kholy, Mrs Ann Halford, Ms Teresa Ng and Mr Hassan Patel for their constant and patient support. • My two PhD examiners, Professor Duncan Gillies and Dr. Nigel Goddard, for their at- tention to detail. • Last but not least, my wonderful wife, Ginnie Kelly, for her love, support, patience and encouragement. I really do love you with all my heart, Ginnie. Thank you. vi Dedication To my two children, Max and Olive. vii “One concern I’ve always had is that most people have no idea where their energy goes, so any attempt to conserve is like optimizing a program without a profiler.” Bret Victor 2015 viii Contents Abstract i Copyright iii Acknowledgementsv 1 Introduction1 1.1 Brief overview of energy disaggregation.......................1 1.1.1 The many names of ‘energy disaggregation’.................2 1.1.2 The NILM research community.......................3 Companies offering NILM products.....................4 1.2 Objectives....................................... 10 1.3 Motivation: proposed benefits of NILM....................... 12 1.3.1 A large reduction in CO2 emissions is required to maintain a stable climate 12 1.3.2 Demand reduction is the most cost-effective way to reduce CO2 emissions 14 1.3.3 Smart meters are rolling out in many countries............... 14 1.3.4 Energy feedback can reduce energy consumption.............. 15 1.3.5 Use-cases for NILM.............................. 16 Generating itemised energy bills to help people to save energy...... 16 Energy literacy............................ 18 Detailed logs of appliance usage....................... 18 “Did I leave something on?”......................... 18 Personalised energy saving recommendations................ 19 Identify malfunctioning or inefficient appliances.............. 19 Commercial and industrial consumers.................... 19 Help architects to design energy-efficient buildings............. 20 Allow grid operators to improve predictions of energy demand...... 21 ix x CONTENTS Enable higher accuracy whole-system energy models............ 21 Allow utility companies to better segment their users........... 22 Track changes in energy demand in response to interventions....... 22 Targeted demand side response....................... 22 Appliance recognition............................. 25 Enable large-scale surveys into energy usage behaviour.......... 25 Apportion energy consumption to individuals or activities........ 25 Occupancy

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