Entropy-Based Metrics for Occupancy Detection Using Energy Demand
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Abstract:
Smart Meters provide detailed energy consumption data and rich contextual information that can be utilized to assist electricity providers and consumers in understanding and managing energy use. The detection of human activity in residential households is a valuable extension for applications, such as home automation, demand side management, or non-intrusive load monitoring, but it usually requires the installation of dedicated sensors. In this paper, we propose and evaluate two new metrics, namely the sliding window entropy and the interval entropy, inspired by Shannon’s entropy in order to obtain information regarding human activity from smart meter readings. We emphasise on the application of the entropy and analyse the effect of input parameters, in order to lay the foundation for future work. We compare our method to other methods, including the Page–Hinkley test and geometric moving average, which have been used for occupancy detection on the same dataset by other authors. Our experimental results, using the power measurements of the publicly available ECO dataset, indicate that the accuracy and area under the curve of our method can keep up with other well-known statistical methods, stressing the practical relevance of our approach.Keywords:
Smart meter
Occupancy
In the UK, the traditional settlement process aims to allocate customers' total electricity consumption into each half-hour slot based on the typical load profiles. However, the large difference between load profiles and the real energy consumption lead to the unfair and inaccurate electricity charges. In order to tackle this problem, the smart meter will be introduced and this paper, for the first time, accurately quantifies the improvement of using smart metering data in electricity settlement. The result shows that all of the customers are being mischarged with an average of 9.52 GBP overcharged and 10.34 GBP undercharged per household, which will be discussed in terms of potential causes and errors.
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Smart meter
Settlement (finance)
Electricity meter
Consumption
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Questions: What is the shape of occupancy trajectories in fossil organisms? And what is the effect of occupancy on species survival? Data studied: Occupancy and its course through time for a species in extinct large mammal communities from Italy. Search method: We tested if occupancy (the proportion of fossil sites representing a given paleocommunity where a species is present) patterns in extinct communities match a bimodal distribution as in living communities. Then we regressed occupancy on species duration to estimate its effect on long-term survival. We built a null model of random occupancy trajectories and compared it to real data. Conclusions: The occupancy–frequency distribution in extinct communities is either bimodal or right skewed. We found a positive relationship between high occupancy and species survival. We found peaked occupancy trajectories to be the norm for hoofed mammals at least.
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Abundance-occupancy relationships predict that species that occupy more sites are also more locally abundant, where occupancy is usually estimated following the assumption that species can occupy all sampled sites. Here we use the National Ecological Observatory Network small-mammal data to assess whether this assumption affects abundance-occupancy relationships. We estimated occupancy considering all sampled sites (traditional occupancy) and only the sites found within the species geographic range (spatial occupancy) and realized environmental niche (environmental occupancy). We found that when occupancy was estimated considering only sites possible for the species to colonize (spatial and environmental occupancy) weaker abundance-occupancy relationships were observed. This shows that the assumption that the species can occupy all sampled sites directly affects the assessment of abundance-occupancy relationships. Estimating occupancy considering only sites that are possible for the species to colonize will consequently lead to a more robust assessment of abundance-occupancy relationships.
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This paper presents a novel methodology for the incremental characterization and prediction of electricity consumption based on smart meter readings. A self-learning algorithm is developed to incrementally discover patterns in a data stream environment and sustain acquired knowledge for subsequent learning. It generates an evolving columnar structure composed of learning outcomes from each phase. This columnar structure characterizes electricity consumption and thus exposes significant patterns and continuity over time. The proposed technique is applied to smart meter data collected from RMIT University premises. Results show the potential for incremental pattern characterization learning in electricity consumption analysis and forecasting.
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Electricity meter
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Occupancy information in buildings is a crucial information to enable automated load controlling resulting in significant energy savings. Unfortunately, current methods obtain occupancy data by using additional infrastructure, which can be expensive and inefficient. In this paper, we propose a method to predict room-level occupancy by utilizing only smart-meter data. Several classifiers are used to estimate room-level occupancy information. We identify the best feature set consisting of appliances energy data, appliances state, and house-level occupancy data. The features are obtained using only smart meter data along with non-intrusive load monitoring and house-level occupancy prediction. We show that the proposed methods can achieve up to 90% accuracy for room-level occupancy prediction using only smart meter data.
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Occupancy information is useful for efficient energy management in the building sector. The massive high-resolution electrical power consumption data collected by smart meters in the advanced metering infrastructure (AMI) network make it possible to infer buildings' occupancy status in a non-intrusive way. In this paper, we propose a deep leaning model called ABODE-Net which employs a novel Parallel Attention (PA) block for building occupancy detection using smart meter data. The PA block combines the temporal, variable, and channel attention modules in a parallel way to signify important features for occupancy detection. We adopt two smart meter datasets widely used for building occupancy detection in our performance evaluation. A set of state-of-the-art shallow machine learning and deep learning models are included for performance comparison. The results show that ABODE-Net significantly outperforms other models in all experimental cases, which proves its validity as a solution for non-intrusive building occupancy detection.
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Smart meter
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Electricity meter
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This paper describes an investigation of the effect on electric lighting demand of applying occupancy models of various resolution to climate-based daylight modelling. The lighting demand was evaluated for a building zone with the occupant always present, with occupancy corresponding to absence factors, based on an estimated annual mean occupancy, based on estimated 1-hour mean occupancy, and based on 2-min occupancy intervals. The results showed little difference in the annual electric lighting demand when the same occupancy profile was used every day, as opposed to when profiles were used where occupancy varied every day. Furthermore, the results showed that annual electric lighting demand was evaluated slightly conservatively when a mean absence factor was applied as opposed to using dynamic occupancy profiles.
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Daylight
Electric light
Peak demand
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Smart metering is a quite new topic that has grown in importance all over the world and it appears to be a remedy for rising prices of electricity. Forecasting electricity usage is an important task to provide intelligence to the smart gird. Accurate forecasting will enable a utility provider to plan the resources and also to take control actions to balance the electricity supply and demand. The customers will benefit from metering solutions through greater understanding of their own energy consumption and future projections, allowing them to better manage costs of their usage. In this proof of concept paper, our contribution is the proposal for accurate short term electricity load forecasting for 24 hours ahead, not on the aggregate but on the individual household level.
Smart meter
Metering mode
Consumption
Automatic meter reading
Mains electricity
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