EPC Data Research Articles

Simulation of EPC consortium partnership stability and data based on prospect theory

Simulation of EPC consortium partnership stability and data based on prospect theory

Decarbonising at scale: Extracting strategic thinking from EPC and deprivation data

Decarbonising the built environment is at the heart of many nations' route to net zero. This leads to policies that target specific technologies. Within such policies, there is a natural instinct to combine the need to reduce carbon emissions with solving other issues, such as fuel poverty. Here, we examine for the first time if, from a carbon perspective, this is optimal. By assembling energy performance certificates and household economic deprivation data, we use fuzzy matching techniques to produce a single statistically robust dataset of 44,300 households. Then, through secondary data analysis, we closely examine the carbon impact and cost of energy retrofits. Overall, upgrading to band C is the most viable strategy. However, the results demonstrate that households belonging to the least deprived 20% present more than double the carbon saving potential compared to those in the most deprived 20% (2.7 and 1.2tCO2/yr, respectively), and offer the best return in CO2 savings on money spent. This highlights the need for retrofitting policy to be cognisant of both building stock and deprivation and the disproportionate role in climate change played by the more affluent. The results offer important new insights for governments and suggest a rethinking of retrofit initiatives. Practical Application: This study is the first to employ such data to identify retrofit strategies for governments and offers three key practical applications. (i) It shows how by combining such data one can start to develop policy that is tuned to the demographics and stock, and that by disaggregating the data a lot can be learnt prior to the development of local or national policy. (ii) It clearly puts to bed the idea that attacking fuel poverty is the most effective way towards carbon reductions. (iii) It suggests a new way of thinking about targeted interventions that optimise carbon reduction in a cost-effective way.

Simulation of EPC Consortium Partnership Stability and Data Based on Prospect Theory

Simulation of EPC Consortium Partnership Stability and Data Based on Prospect Theory

Multi-dimensional Anti-counterfeiting Anomaly Monitoring Model Based on RFID in Supply Chain

There are two kinds of abnormal conditions in the RFID-enabled supply chain such as forgery and miss operation, but the existing anti-counterfeiting model is not used to distinguish them. In this regard, based on the definition of abnormal activity and its dependencies, this paper proposes the abnormality detection rules of the “frequent pattern” yielded by big data techniques. From a multi-dimensional perspective, combined with EPC data and prior information sent from the previous nodes, it can effectively distinguish between forgery activities and operational anomalies. Consequently, asecure visualization system for anti-counterfeiting and anomaly monitoring in rfid-enabled supply chain is implemented with satisfactory results

Targeting and modelling urban energy retrofits using a city-scale energy mapping approach

City authorities, community groups and retrofit installers need to identify suitable local areas and dwellings for installing energy retrofit measures. This paper presents a localised Geographical Information System (GIS) based approach that utilizes publicly-available national and local datasets on housing and energy to provide targeted low carbon measures across UK cities. The study uses a rapid city-level energy assessment approach to spatially identify suitable neighbourhoods for particular retrofit measures, based on relative energy use and fuel poverty ratings. A GIS-based carbon mapping model (called DECoRuM) is then used to estimate energy use and potential for energy reduction on a house-by-house level. The improvement measures are aggregated to encourage bulk installations and drive down installation costs.To identify an appropriate neighbourhood case study area, publicly available datasets were assessed for the town of Bicester (Oxfordshire, UK), which included Ordnance Survey Mastermap, Energy Performance Certificate data (EPC) and sub-national energy statistics available at lower layer super output area (LSOA). When the EPC data for Bicester were compared with the sub-national statistics for Bicester, the average difference was found to be ∼800 kWh. This is interesting as EPCs represent dwelling specific but modelled data whereas sub-national datasets represent actual but aggregated data. Superimposing the above datasets, a neighbourhood in southwest Bicester was selected as having the highest percentage of dwellings with energy consumption >300 kWh/m2/yr (EPC), most dwellings in need of wall insulation (EPC), second highest mean total energy consumption (sub-national), and third highest percentage of fuel poor dwellings (sub-national). House-level energy assessment in the selected area using DECoRuM showed that a package based approach comprising fabric and heating system upgrade and solar PVs emerged as the most effective.

Inspection of harmful microbial contamination occurred in edible salmon flesh using imaging technology

The total counts of Enterobacteriaceae and Pseudomonas spp. (EPC) appeared on edible salmon flesh were determined by using near-infrared (NIR) (900–1700nm) hyperspectral imaging. Three-dimensional hyperspectral images (x,y,λ) of salmon samples were acquired at different storage time. Spectra (λ) extracted in reflectance (R) unit and two other transformed spectra units, absorbance (A) and Kubelka–Munck (KM), were prepared to relate to the measured EPC data by using partial least square (PLS) regression. Based on the three spectra parameters, three full wavelength PLS models defined as FR-PLS, FA-PLS and FKM-PLS were developed with all correlation coefficients of prediction (RP) over 0.900. To simplify these models, wavelengths holding the most important information were selected by executing competitive adaptive reweighted sampling (CARS) algorithm. Better performance was found in the resulting simplified R-PLS model (defined as FRS-PLS model) which was established with only nine important wavelengths (931, 1138, 1175, 1242, 1359, 1628, 1641, 1652 and 1655nm) selected from R spectra. The absolute difference between root mean square errors of calibration (RMSEC) and prediction (RMSEP) in the FRS-PLS model was 0.063, less than half (44%) of that of the original FR-PLS model. By applying the FRS-PLS model to the 2-D images (x,y), EPC distribution maps were generated to visualize the spatial variation of EPC and the adaptability of the FRS-PLS model for EPC evaluation was further demonstrated with these distribution maps in which different colors indicated different degrees of EPC contamination. To sum up, NIR hyperspectral imaging technology shows a great potential to predict the EPC contamination in salmon flesh. In view of the results obtained from this study, a multi-spectral imaging system could be developed and further refined for online detection applications in fish industry.