Aphids and their parasitoids on the Canary grass, Phalaris canariensis in Malta (Hymenoptera, Braconidae, Aphidiinae)

Adialytus ambiguus and Diaeretiella rapae were reared from Rhopalosiphum padi on the Canary grass, Phalaris canariensis in Malta. The identity to species level of the Adialytus required confirmation via DNA analysis of the respective species group. Some ecosystem interrelationships derived from the determined food webs are discussed.peer-reviewe

An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study

Smart meter roll-outs provide easy access to granular meter measurements, enabling advanced energy services, ranging from demand response measures, tailored energy feedback and smart home/building automation. To design such services, train and validate models, access to data that resembles what is expected of smart meters, collected in a real-world setting, is necessary. The REFIT electrical load measurements dataset described in this paper includes whole house aggregate loads and nine individual appliance measurements at 8-second intervals per house, collected continuously over a period of two years from 20 houses. During monitoring, the occupants were conducting their usual routines. At the time of publishing, the dataset has the largest number of houses monitored in the United Kingdom at less than 1-minute intervals over a period greater than one year. The dataset comprises 1,194,958,790 readings, that represent over 250,000 monitored appliance uses. The data is accessible in an easy-to-use comma-separated format, is time-stamped and cleaned to remove invalid measurements, correctly label appliance data and fill in small gaps of missing data

How to make efficient use of kettles : understanding usage patterns

According to a survey by the Energy Savings Trust three-quarters of UK households overfill their kettle, wasting GBP68 million per year. This paper focuses on patterns of behaviour with respect to kettle use and how these could be influenced by providing feedback to make kettle usage more efficient. Firstly, we study how kettles are used across 14 UK households for a two-year period, which allows analysis of seasonal patterns as well as changes due to the holiday season. We also examine usage patterns based on the type of occupant and how their daily routines affect usage. Secondly, a case study is described where a standard kettle has been replaced with an ‘eco’ kettle during the monitoring period, which allows to analyse if energy consumption has been reduced due to using a more energy efficient kettle. We look at the usage patterns and investigate potential change in behaviour that has occurred since the switch. Our main findings based on monitoring diverse UK homes with a range of kettles, is that the total consumption is less dependent on the type of kettle used, and more dependent on the established household usage patterns and habits. We also show, through our case study, that usage of kettles can be improved by optimising usage patterns to best utilise the type of kettle

Smart homes, control and energy management:How do smart home technologies influence control over energy use and domestic life?

By introducing new ways of automatically and remotely controlling domestic environments smart technologies have the potential to significantly improve domestic energy management. It is argued that they will simplify users’ lives by allowing them to delegate aspects of decision-making and control - relating to energy management, security, leisure and entertainment etc. - to automated smart home systems. Whilst such technologically-optimistic visions are seductive to many, less research attention has so far been paid to how users interact with and make use of the advanced control functionality that smart homes provide within already complex everyday lives. What literature there is on domestic technology use and control, shows that control is a complex and contested concept. Far from merely controlling appliances, householders are also concerned about a wide range of broader understandings of control relating, for example, to control over security, independence, hectic schedules and even over other household members such as through parenting or care relationships. This paper draws on new quantitative and qualitative data from 4 homes involved in a smart home field trial that have been equipped with smart home systems that provide advanced control functionality over appliances and space heating. Quantitative data examines how householders have used the systems both to try and improve their energy efficiency but also for purposes such as enhanced security or scheduling appliances to align with lifestyles. Qualitative data (from in-depth interviews) explores how smart technologies have impacted upon, and were impacted by, broader understandings of control within the home. The paper concludes by proposing an analytical framework for future research on control in the smart home

Transparent AI : explainability of deep learning based load disaggregation

The paper focuses on explaining the outputs of deep-learning based non-intrusive load monitoring (NILM). Explainability of NILM networks is needed for a range of stakeholders: (i) technology developers to understand why a model is under/over predicting energy usage, missing appliances or false positives, (ii) businesses offering energy advice based on NILM as part of a broader energy home management recommender system, and (iii) end-users who need to understand the outcomes of the NILM inference

Understanding domestic appliance use through their linkages to common activities

Activities are a descriptive term for the common ways households spend their time. Examples include daily routines such as cooking, doing laundry, and Computing. Smart energy meter data can be used to generate time profiles of activities that are meaningful to households’ own lived experience. Activities are therefore a lens through which energy feedback to households can be made salient and understandable. This paper demonstrates how hourly time profiles of household activities can be inferred from smart energy meter data, supplemented by appliance monitors and environmental sensors. In-depth interviews and home surveys are used to identify appliances and devices used for a range of activities. These relationships between te chnologies and activities are captured in an ‘activity ontology’ that can be applied to smart meter data to make inferences on hourly time profiles of up to nine everyday activities. Results are presented from six homes participating in a UK trial of smart home technologies. The duration of activities and when they are carried out is examined within households. The time profile of domestic activities has routine characteristics but these tend to vary widely between households with different socio-demo graphic characteristics. Analysing the energy consumption associated with different activities leads to a useful means of providing activity-itemised energy feedback, and also reveals certain households to be high energy-using across a range of activities

A Novel Discrete Dimming Ballast for Linear Fluorescent Lamps

A novel discrete dimming ballast for linear fluorescent lamps is proposed in this paper. A proposed dimming control circuit is combined with a ballast module for multiple lamps to realize control of three discrete lighting levels. Compared with conventional step dimming or ON-OFF control methods, the proposed discrete dimming method has the following advantages: 1) digital signal is generated by the dimming control circuit to control the lamps\u27 turn- ON and -OFF, which makes the system more reliable and integrated; 2) the proposed discrete dimming system replaces relays, which are necessary in conventional lamp ON-OFF control, and therefore decreases the system cost; 3) the proposed dimming ballast can be installed by keeping the original wiring system. This makes the upgrading of a lighting system more effective and efficient; 4) the dimming control circuit also provides a good isolation for operating the low-voltage wall switches by hand safely. Both theoretical, simulation, and experimental results are in good agreement

Semi-supervised seismic event detection using Siamese Networks

Detecting seismic events and their precursors is vital to understand and assess risks in areas of seismic instability. Most recent detection methods are based on supervised learning, where machine learning models are first trained using a labelled dataset, before being deployed. However, seismic sensors are often difficult to install and maintain, and large-scale events are few and far between. Furthermore, labelling collected data requires a great deal of time and effort from seismologists. Noise can vastly increase the difficulty of this task and labels can be highly subjective. Labelled data used for training machine learning models depends on the monitoring setup and geological characteristics of the terrain where the sensors are installed. For example, a dataset of events recorded in the Alps will likely not be representative of events that could be seen in less mountainous regions, meaning that transferability of proposed networks is vital. The Rest and Be Thankful in Scotland is a remote hillside prone to weather-induced seismic events which can cause disruption to the road infrastructure in the valley below, after rockfalls and landslides due to quakes. In this paper we propose a semi-supervised method of clustering these different types of events. Grouping data into categories of both known and unknown event types can reduce the time needed by experts to create labelled datasets via the use of Siamese networks and further understand the dynamics of the slope. We validate results against the BGS earthquake database from within a 50km radius, as well as human induced rockfalls. Grouping across around 100 days of data has detected a possible 10 earthquakes, 82 rockfalls, and 137 micro-quakes

A data management platform for personalised real-time energy feedback

This paper presents a data collection and energy fe edback platform for smart homes to enhance the value of information given by smart energy meter da ta by providing user-tailored real-time energy consumption feedback and advice that can be easily accessed and acted upon by the household. Our data management platform consists of an SQL server back-end which collects data, namely, aggregate power consumption as well as consumption of major appliances, temperature, humidity, light, and motion data. These data streams allow us to infer information about the household’s appliance usage and domestic activities, which in t urn enables meaningful and useful energy feedback. The platform developed has been rolled ou t in 20 UK households over a period of just over 21 months. As well as the data streams mentioned, q ualitative data such as appliance survey, tariff, house construction type and occupancy information a re also included. The paper presents a review of publically available smart home datasets and a desc ription of our own smart home set up and monitoring platform. We then provide examples of th e types of feedback that can be generated, looking at the suitability of electricity tariffs a nd appliance specific feedback

Transferability of neural networks approaches for low-rate energy disaggregation

Energy disaggregation of appliances using non-intrusive load monitoring (NILM) represents a set of signal and information processing methods used for appliance-level information extraction out of a meter's total or aggregate load. Large-scale deployments of smart meters worldwide and the availability of large amounts of data, motivates the shift from traditional source separation and Hidden Markov Model-based NILM towards data-driven NILM methods. Furthermore, we address the potential for scalable NILM roll-out by tackling disaggregation complexity as well as disaggregation on houses which have not been 'seen' before by the network, e.g., during training. In this paper, we focus on low rate NILM (with active power meter measurements sampled between 1-60 seconds) and present two different neural network architectures, one, based on convolutional neural network, and another based on gated recurrent unit, both of which classify the state and estimate the average power consumption of targeted appliances. Our proposed designs are driven by the need to have a well-trained generalised network which would be able to produce accurate results on a house that is not present in the training set, i.e., transferability. Performance results of the designed networks show excellent generalization ability and improvement compared to the state of the art