Abstract. Many countries are rolling out smart electricity meters. These measure a home's total power demand. However, research into consumer behaviour suggests that consumers are best able to improve their energy efficiency when provided with itemised, appliance-by-appliance consumption information. Energy disaggregation is a computational technique for estimating appliance-by-appliance energy consumption from a whole-house meter signal. To conduct research on disaggregation algorithms, researchers require data describing not just the aggregate demand per building but also the 'ground truth' demand of individual appliances. In this context, we present UK-DALE: an open-access dataset from the UK recording Domestic Appliance-Level Electricity at a sample rate of 16 kHz for the whole-house and at 1 /6 Hz for individual appliances. This is the first open access UK dataset at this temporal resolution. We recorded from five houses, one of which was recorded for 655 days, the longest duration we are aware of for any energy dataset at this sample rate. We also describe the low-cost, open-source, wireless system we built for collecting our dataset. Prudent management of electricity consumption is becoming increasingly important. Yet studies on residential energy users show that the vast majority are poor at estimating their whole-house energy consumption or the energy consumption of individual devices 1 . Residents often underestimate the energy used by heating and overestimate the consumption of perceptually salient devices like lights and televisions 1 . Residents' failure to correctly estimate energy consumption is likely to lead to higher total consumption. How significant is occupant behaviour in determining total energy usage? Energy use can differ by two or three times among identical houses with similar appliances occupied by people from similar demographics 2,3,4 . These large differences in energy consumption are attributed to differences in consumption behaviour. If the house provided better feedback about which devices used the most energy then users could adjust their behaviour to make more efficient use of appliances. 'Smart electricity meters' are one such feedback mechanism. The UK Government requires that energy retailers install smart electricity and gas meters by 2020. The roll-out is already underway 5 . Similar smart meter roll-outs are planned in many countries. The business case for smart meters in Great Britain 6 assumes that smart meters will drive savings of £4.6 billion due to reduced energy consumption (across both electricity and gas). Smart meters only provide energy consumption measurements for the entire house yet behavioural research suggests that consumers are best able to manage their electricity consumption when given appliance-by-appliance information 7 . Energy disaggregation aims to estimate appliance-by-appliance consumption from a smart meter signal and hence may play an important role in realising the energy savings projected by the smart meter business case. Energy disaggregation 8 is an active area of research (see Armel et al. 2013 9 for a recent review). Researchers require access to large datasets recorded in the field to develop disaggregation algorithms but it is not practical for every researcher to record their own dataset. Hence the creation of open access datasets is key to promote a vibrant research community. . To test the performance of a disaggregation algorithm for a specific country, it is important to have access to data from that country because electricity usage varies significantly between countries; both because different countries use different sets of appliances and also because different cultures show different usage patterns. At the time of writing, the only open-access dataset recorded in the UK is the DECC/DEFRA Household Electricity Study 19 which has a sample period of two minutes. Yet this sample rate is 12 times slower than UK smart meters which will sample every 10 seconds 23 . It is this smart meter data which will provide the input to disaggregation algorithms so researchers require access to 10 second data to design disaggregation algorithms for the UK (some other countries will also use smart meters with similar sample periods). We present the first open access UK dataset with a high temporal resolution. We recorded from five houses. Every six seconds we recorded the active power drawn by individual appliances and the whole-house apparent power demand. Additionally, in three houses, we sampled the whole-house voltage and current at 44.1 kHz (down-sampled to 16 kHz for storage) and also calculated the active power, apparent power and RMS voltage at 1 Hz. In House 1, we recorded for 655 days and individually recorded from almost every single appliance in the house resulting in a recording of 54 separate channels (although less channels were recorded towards the start of the dataset). We will continue to record from this house for the foreseeable future. We recorded from the four other houses for several months; each of these houses recorded between 5 to 26 channels of individual appliance data. Figure 1 provides an overview of the system design and Table 1 summarises the dataset. This dataset may also be of use to researchers working on: • modelling the electricity grid. • exploring the potential for automated demand response. • appliance usage behaviour. 1
The first work that we are aware of that uses HMMs is REF , where the authors apply a Factorial HMM (FHMM) to the energy disaggregation task.
The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes
Scientific Data 2 (2015) Article number: 150007 (2015)
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