Utilizing smart home data to support the reduction of energy demand from space heating – insights from a UK field study

It is anticipated that the wider deployment of Smart Home systems will give building occupants improved control and automation capabilities over building conditions, services and equipment. These smart technologies will also provide numerous streams of data which could help to identify opportunities to reduce energy demand in homes. This paper explores this topic by focusing on data gathered from Smart Home systems, installed in a sample of five UK homes, which provide occupants with advanced zonal space heating control. Initial results suggest that Smart Home data can generate useful information to assist energy demand reduction; including the identification of excessive heat loss from specific rooms, periods of unoccupied heating, and heating system characteristics that lead to suboptimal heating patterns. Practical issues encountered during the field study highlight important social and contextual factors that can influence the quality of data recorded. These factors could potentially impede the wider adoption of Smart Home technologies with zonal heating functions. This work is continuing and the next steps are to calculate the energy savings which would result after data from Smart Home systems was used to identify inefficient homes, systems or practices

Identifying the time profile of everyday activities in the home using smart meter data

Activities are a descriptive term for the common ways households spend their time. Examples include cooking, doing laundry, or socialising. Smart 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 a multi-step methodology for inferring hourly time profiles of ten household activities using smart meter data, supplemented by individual appliance plug monitors and environmental sensors. First, household interviews, video ethnography, and technology surveys are used to identify appliances and devices in the home, and their roles in specific activities. Second, ‘ontologies’ are developed to map out the relationships between activities and technologies in the home. One or more technologies may indicate the occurrence of certain activities. Third, data from smart meters, plug monitors and sensor data are collected. Smart meter data measuring aggregate electricity use are disaggregated and processed together with the plug monitor and sensor data to identify when and for how long different activities are occurring. Sensor data are particularly useful for activities that are not always associated with an energy-using device. Fourth, the ontologies are applied to the disaggregated data to make inferences on hourly time profiles of ten everyday activities. These include washing, doing laundry, watching TV (reliably inferred), and cleaning, socialising, working (inferred with uncertainties). Fifth, activity time diaries and structured interviews are used to validate both the ontologies and the inferred activity time profiles. Two case study homes are used to illustrate the methodology using data collected as part of a UK trial of smart home technologies. The methodology is demonstrated to produce reliable time profiles of a range of domestic activities that are meaningful to households. The methodology also emphasises the value of integrating coded interview and video ethnography data into both the development of the activity inference process

Supporting retrofit decisions using smart meter data: a multi-disciplinary approach

The UK Government’s flagship energy efficiency program, the Green Deal, provides retrofit advice for household occupants based on a technical house survey and an engineering modelling tool. Smart meter data provides an opportunity to give bespoke advice to occupants based on the actual performance of their home and their own heating practices as well as visualisations of hourly and daily energy use. This work presents initial results from one component of a complex multidisciplinary research project which aims to use smart meter and smart home data to design and develop retrofit decision support concepts. Home visits involving creative design based research activities were carried out in five homes. Household occupants were presented with two types of energy use report; 1) a Green Deal advice report which includes suggested retrofit measures and annual energy consumption figures based on a steady state modelling approach and; 2) a personalised energy use report, based on smart meter data collected in their homes over a 12 month period. The home visits were carried out with the occupants to discuss a range of possible retrofit measures and gather feedback regarding the communication method for advice about energy efficiency improvements. Initial findings from the home visits indicate that the provision of energy feedback using smart meter data did not directly influence the occupants to make energy efficient retrofits any more than the Green Deal advice reports. However, the visualisation of actual hourly and daily energy use enabled householders to make links with their lived experience and stimulated discussions about their energy use which may impact on their preconceived ideas about energy use and energy efficiency measures

Participant characteristics with data available for work stress and cortisol secretion at Whitehall II Phase 7 (2002–2004) ^#.

<p>^#Within the 2,126 participants included in current analysis, 2,094 and 2,090 had complete data for job strain and ERI measures, respectively. CAR, cortisol awakening response; Slope, cortisol decline across the day.</p><p>^b Cortisol data adjusted for age, gender, ethnicity.</p><p>* P<0.05, ** P<0.01.</p

Salivary cortisol levels at waking and 30-min-later by job-demand in women and men.

<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081020#pone-0081020-g002" target="_blank">Figure 2</a>. Salivary cortisol levels (adjusted means including 95% CI) at waking and 30-min later by job demand status in women and men, adjusted for age, gender, ethnicity, time of waking and time since waking. SD: standard deviation.</p

Diurnal cortisol decline by Effort-Reward-Imbalance (ERI) status.

<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081020#pone-0081020-g001" target="_blank">Figure 1</a>. Diurnal cortisol decline (adjusted means including 95% CI) by ERI status, adjusted for age, gender, ethnicity, time of waking and time since waking. ERI: effort-reward-imbalance ratio; SD: standard deviation.</p

Participant Characteristics at Whitehall II Phase 7 (2002–2004).

<p>SD: standard deviation.</p

Correlation matrix for the work stress measures within participants included in analysis.

<p>The spearman rank correlation coefficient (P) are reported</p>*<p>p<0.001, ** p<0.05</p

Measures of work stress and cortisol secretion measures in all participants at Whitehall II Phase 7, adjusted for age, gender, ethnicity, time of waking and time since waking.

<p>CAR, cortisol awakening response; Slope, cortisol decline over the day</p><p>Data were presented by 1-Standard Deviation increase of each dimension of JDC/ERI models. Job strain was calculated by subtracting control score from demand score; ERI ratio was calculated by the formula <i>effort/reward*0.5</i> and logarithm transformed.</p

Neoproterozoic granitic gneiss offshore the Shandong Peninsula of Eastern China: the eastward extension of the Sulu Orogenic Belt

<p>The Sulu Orogenic Belt in eastern China has experienced a multistage tectonic evolutionary history. However, its geological evolution has not yet been corroborated by sufficient direct evidence from basement rocks. Chaolian Island on the Qianliyan Uplift provides an opportunity to study the formation and evolution of the Sulu Orogenic Belt using direct geochronological and geochemical evidence. We determined that the characteristic mineral assemblage in the study region is quartz + K-feldspar + perthite + biotite + muscovite. The samples are silica- (SiO₂ = 72.8%–75.8%) and alkali-rich (ALK(Na₂O+K₂O) = 8.7%–9.3%), with high iron-magnesium ratios (FeO*/(FeO*+MgO) = 0.92–0.96) and low CaO and MgO concentrations. Furthermore, they are rich in large-ion lithophile elements K, Rb, Ba, and U, but depleted in high field strength elements Nb, Ta, and Zr. They exhibit high Ga/Al values (Ga × 10⁴/Al = 3.33–3.74) and significant fractionation between light and heavy rare earth elements. The samples are A-type granites. In the discrimination diagrams for granite genesis types, the samples plotted in the post-orogenic A2-type granite region. Secondary ion mass spectrometer (SIMS) zircon U–Pb dating results indicated that the granitic gneiss formed ~782.6–802.3 Ma (Middle Neoproterozoic), consistent with the timing of the breakup of the Rodinia supercontinent on the northeastern margin of the Yangtze Plate. Comparing geochemical characteristics and zircon U–Pb ages of the A-type granitic gneisses of the Sulu Orogenic Belt, the Qianliyan Uplift appears to be an extension of the belt across the ocean and is affiliated with the Yangtze Plate. The granitic gneiss on Chaolian Island is related to the formation of a mantle superplume during the breakup of Rodinia, and the northeastern margin of the Yangtze Plate during the Middle Neoproterozoic was located in a back-arc extension setting induced by the subduction of oceanic plates.</p