Energy Disaggregation for SMEs using Recurrence Quantification Analysis

Energy disaggregation determines the energy consumption of individual appliances from the total demand signal, which is recorded using a single monitoring device. There are varied approaches to this problem, which are applied to different settings. Here, we focus on small and medium enterprises (SMEs) and explore useful applications for energy disaggregation from the perspective of SMEs. More precisely, we use recurrence quantification analysis (RQA) of the aggregate and the individual device signals to create a two-dimensional map, which is an outlined region in a reduced information space that corresponds to ‘normal’ energy demand. Then, this map is used to monitor and control future energy consumption within the example business so to improve their energy efficiency practices. In particular, our proposed method is shown to detect when an appliance may be faulty and if an unexpected, additional device is in use

Electric vehicles and low-voltage grid: impact of uncontrolled demand side response

The authors are looking at the impact of electric vehicles (EV) charging from low-voltage (LV) networks. Based on the data obtained from two different pilot projects: (i) Mini-E trial where EV users were incentivised to charge during the night; (ii) My Electric Avenue trial, where there were no similar incentives, authors want to quantify the impact of EV charging, presuming that the number of home-charging EV users will increase significantly in the near future. By assuming that the current load at individual household level is known or inferred, simulations are performed to estimate the future load. The authors look at different percentages of EV uptake and model clustered scenarios, where the social networking effect is imposed – users adopt an EV with a higher probability if their neighbour already has one. Simulations demonstrate that incentivising night-time charging can create large new peaks during the night, which could have negative effects on LV networks. On the other hand, simulations based on the data with no incentives shows that naturally occurring diversity in charging behaviour does not automatically result in comparable network stress at the same penetrations

An innovation diffusion model of a local electricity network that is influenced by internal and external factors

Haynes et al. (1977) derived a nonlinear differential equation to determine the spread of innovations within a social network across space and time. This model depends upon the imitators and the innovators within the social system, where the imitators respond to internal influences, whilst the innovators react to external factors. Here, this differential equation is applied to simulate the uptake of a low-carbon technology (LCT) within a real local electricity network that is situated in the UK. This network comprises of many households that are assigned to certain feeders. Firstly, travelling wave solutions of Haynes’ model are used to predict adoption times as a function of the imitation and innovation influences. Then, the grid that represents the electricity network is created so that the finite element method (FEM) can be implemented. Next, innovation diffusion is modelled with Haynes’ equation and the FEM, where varying magnitudes of the internal and external pressures are imposed. Consequently, the impact of these model parameters is investigated. Moreover, LCT adoption trajectories at fixed feeder locations are calculated, which give a macroscopic understanding of the uptake behaviour at specific network sites. Lastly, the adoption of LCTs at a household level is examined, where microscopic and macroscopic approaches are combined

Forecasting Impact: a case study of bioenergy systems

One of the main drivers for the use of bioenergy is the reduction in GHG emissions. Bioenergy is a versatile energy source that is not only storable, but is also able to be used in many ways, for example as fuel for transport, electricity and/or heat. There are advantages (and disadvantages) of using bioenergy for each vector, and impacts vary due to region, technology, and pathways. However, there is not enough bioenergy to meet all of our energy demand and therefore it must be used alongside other energy sources. Determining how to optimise its use is critical for policy makers and industry.In order to understand the optimal use for bioenergy, assessments have been undertaken on several pathways to determine their life cycle impacts. Nevertheless, bioenergy is a fast evolving area with new pathways and feedstock utilisation being developed and proposed at pace. At the other end of the scale, governments and policy makers are trying to determine the best use of existing resources and the impact of (semi) disruptive systems. Exploring the impact of varying emerging and existent bioenergy vectors, not just in terms of production, but in terms of their ability to disrupt current systems, is complex. This research explores the use of bioenergy in heating systems. Several anticipatory pathways are explored, for example the production and use of biogas in existing gas networks and the use of bioenergy on a more local scale for heating through CHP. Without CCS, bioenergy still emits GHGs during its use. However, as CO2 can be reabsorbed by replacement feedstocks over a relevant timescale, its impact is arguably less.This is questioned by some; but what is clear is that timescale is critical. The recent IPCC report suggests we have 12 years to limit catastrophic climate change. So any mechanisms we have for optimising systems must consider the short-term impacts as well as the traditionally longer timescale reported within conventional LCA.This work highlights the significance of the differing GHGs on a temporal scale as the time GHGs remain in the atmosphere varies substantially. For example, methane (CH4) has a higher GWP, but a shorter lifetime than CO2. This means that emissions of CH4 will have higher impacts for a shorter period of time, meaning it is a critical emission to manage in order to minimise our immediate impact on climate change.The research demonstrates that GWP impacts should be reported against a range of timescales; or at a minimum, that different GHG emissions should be distinguished and supports the development of a mechanism to identify both longer and critically, shorter, term pathways to GHG reduction

A Bayesian Inverse Approach to Proton Therapy Dose Delivery Verification

This study presents a proof-of-concept for a novel Bayesian inverse method in a one-dimensional setting, aimed at proton beam therapy treatment verification. Our methodology is predicated on a hypothetical scenario wherein strategically positioned sensors detect prompt-{\gamma}'s emitted from a proton beam when it interacts with defined layers of tissue. Using this data, we employ a Bayesian framework to estimate the proton beam's energy deposition profile. We validate our Bayesian inverse estimations against a closed-form approximation of the Bragg Peak in a uniform medium and a layered lung tumour.Comment: 22 pages, 12 figure

Exploring temporal aspects of climate-change effects due to bioenergy

The greenhouse gas emissions associated with bioenergy are often temporally dispersed and can be a mixture of long-term forcers (such as carbon dioxide) and short-term forcers (such as methane). These factors affect the timing and magnitude of climate-change impacts associated with bioenergy in ways that cannot be clearly communicated with a single metric. This is critical as key comparisons that determine incentives and policy for bioenergy are based upon climate-change impacts expressed as carbon dioxide equivalent calculated with GWP100. This paper explores these issues further and presents a spreadsheet tool to facilitate quick assessment of these temporal effects. The potential effect of (i) a mix of GHGs and (ii) emissions that change with time are illustrated through two case studies. In case study 1, variations in the mix of greenhouse gases mean that apparently similar impacts after 100-years, mask radically different impacts before then. In case study 2, variations in the timing of emissions cause their climate-change impacts (integrated radiative-forcing and temperature change) to differ from the impacts that an emissions-balance would suggest. The effect of taking alternative approaches to considering “CO2-equivalence” are also assessed. In both cases, a single metric for climate-change effects was found to be wanting. A simple tool has been produced to help practitioners evaluate whether this is the case for any given system. If complex dynamics are apparent, it is recommended that additional metrics, more detailed inventory, or full time-series impact results are used in order to accurately communicate these climate-change effects.</p

Forecasting Impact: a case study of bioenergy systems

One of the main drivers for the use of bioenergy is the reduction in GHG emissions. Bioenergy is a versatile energy source that is not only storable, but is also able to be used in many ways, for example as fuel for transport, electricity and/or heat. There are advantages (and disadvantages) of using bioenergy for each vector, and impacts vary due to region, technology, and pathways. However, there is not enough bioenergy to meet all of our energy demand and therefore it must be used alongside other energy sources. Determining how to optimise its use is critical for policy makers and industry.In order to understand the optimal use for bioenergy, assessments have been undertaken on several pathways to determine their life cycle impacts. Nevertheless, bioenergy is a fast evolving area with new pathways and feedstock utilisation being developed and proposed at pace. At the other end of the scale, governments and policy makers are trying to determine the best use of existing resources and the impact of (semi) disruptive systems. Exploring the impact of varying emerging and existent bioenergy vectors, not just in terms of production, but in terms of their ability to disrupt current systems, is complex. This research explores the use of bioenergy in heating systems. Several anticipatory pathways are explored, for example the production and use of biogas in existing gas networks and the use of bioenergy on a more local scale for heating through CHP. Without CCS, bioenergy still emits GHGs during its use. However, as CO2 can be reabsorbed by replacement feedstocks over a relevant timescale, its impact is arguably less.This is questioned by some; but what is clear is that timescale is critical. The recent IPCC report suggests we have 12 years to limit catastrophic climate change. So any mechanisms we have for optimising systems must consider the short-term impacts as well as the traditionally longer timescale reported within conventional LCA.This work highlights the significance of the differing GHGs on a temporal scale as the time GHGs remain in the atmosphere varies substantially. For example, methane (CH4) has a higher GWP, but a shorter lifetime than CO2. This means that emissions of CH4 will have higher impacts for a shorter period of time, meaning it is a critical emission to manage in order to minimise our immediate impact on climate change.The research demonstrates that GWP impacts should be reported against a range of timescales; or at a minimum, that different GHG emissions should be distinguished and supports the development of a mechanism to identify both longer and critically, shorter, term pathways to GHG reduction

Skin Pharmacokinetics of Transdermal Scopolamine:Measurements and Modeling

Prediction of skin absorption and local bioavailability from topical formulations remains a difficult task. An important challenge in forecasting topical bioavailability is the limited information available about local and systemic drug concentrations post application of topical drug products. Commercially available transdermal patches, such as Scopoderm (Novartis Consumer Health UK), offer an opportunity to test these experimental approaches as systemic pharmacokinetic data are available with which to validate a predictive model. The long-term research aim, therefore, is to develop a physiologically based pharmacokinetic model (PBPK) to predict the dermal absorption and disposition of actives included in complex dermatological products. This work explored whetherin vitrorelease and skin permeation tests (IVRT and IVPT, respectively), andin vitroandin vivostratum corneum (SC) and viable tissue (VT) sampling data, can provide a satisfactory description of drug “input rate” into the skin and subsequently into the systemic circulation.In vitrorelease and skin permeation results for scopolamine were consistent with the previously reported performance of the commercial patch investigated. New skin sampling data on the dermatopharmacokinetics (DPK) of scopolamine also accurately reflected the rapid delivery of a “priming” dose from the patch adhesive, superimposed on a slower, rate-controlled input from the drug reservoir. The scopolamine concentration versus time profiles in SC and VT skin compartments,in vitroandin vivo, taken together with IVRT release and IVPT penetration kinetics, reflect the input rate and drug delivery specifications of the Scopoderm transdermal patch and reveal the importance of skin binding with respect to local drug disposition. Further data analysis and skin PK modeling are indicated to further refine and develop the approach outlined.</p

Skin Pharmacokinetics of Transdermal Scopolamine:Measurements and Modeling

Prediction of skin absorption and local bioavailability from topical formulations remains a difficult task. An important challenge in forecasting topical bioavailability is the limited information available about local and systemic drug concentrations post application of topical drug products. Commercially available transdermal patches, such as Scopoderm (Novartis Consumer Health UK), offer an opportunity to test these experimental approaches as systemic pharmacokinetic data are available with which to validate a predictive model. The long-term research aim, therefore, is to develop a physiologically based pharmacokinetic model (PBPK) to predict the dermal absorption and disposition of actives included in complex dermatological products. This work explored whetherin vitrorelease and skin permeation tests (IVRT and IVPT, respectively), andin vitroandin vivostratum corneum (SC) and viable tissue (VT) sampling data, can provide a satisfactory description of drug “input rate” into the skin and subsequently into the systemic circulation.In vitrorelease and skin permeation results for scopolamine were consistent with the previously reported performance of the commercial patch investigated. New skin sampling data on the dermatopharmacokinetics (DPK) of scopolamine also accurately reflected the rapid delivery of a “priming” dose from the patch adhesive, superimposed on a slower, rate-controlled input from the drug reservoir. The scopolamine concentration versus time profiles in SC and VT skin compartments,in vitroandin vivo, taken together with IVRT release and IVPT penetration kinetics, reflect the input rate and drug delivery specifications of the Scopoderm transdermal patch and reveal the importance of skin binding with respect to local drug disposition. Further data analysis and skin PK modeling are indicated to further refine and develop the approach outlined.</p