The psychological and economic factors that influence energy consumption habits of low-income earners

Social marketers and governments have often targeted hard to reach or vulnerable groups (Gordon et al., 2006) such as young adults and low income earners. Past research has shown that low-income earners are often at risk of poor health outcomes and diminished lifestyle (Hampson et al., 2009; Scott et al., 2012). Young adults (aged 18 to 35) are in a transition phase of their life where lifestyle preferences are still being formed and are thus a useful target for long-term sustainable change. An area of focus for all levels of government is the use of energy with an aim to reduce consumption. There is little research to date that combines both of these groups and in particular in the context of household energy usage. Research into financially disadvantaged consumers is challenging the notion that that low income consumer purchasing and usage of products and services is based upon economic status (Sharma et al., 2012). Prior research shows higher income earners view items such as televisions and computers as necessities rather than non-essential (Karlsson et al., 2004). Consistent with this is growing evidence that low income earners purchase non-essential, energy intensive electronic appliances such as multiple big screen TV sets and additional refrigerators. With this in mind, there is a need for knowledge about how psychological and economic factors influence the energy consumption habits (e.g. appliances on standby power, leaving appliances turned on, running multiple devices at one time) of low income earners. Thus, our study sought to address the research question of: What are the factors that influence young adult low-income earners energy habits?

The effect of knee flexion contracture following total knee arthroplasty on the energy cost of walking.

This study evaluated the energy cost of walking (Cw) with knee flexion contractures (FC) simulated with a knee brace, in total knee arthroplasty (TKA) recipients (n=16) and normal controls (n=15), and compared it to baseline (no brace). There was no significant difference in Cw between the groups at baseline but TKA recipients walked slower (P=0.048) and with greater knee flexion in this condition (P=0.003). Simulated FC significantly increased Cw in both groups (TKA P=0.020, control P=0.002) and this occurred when FC exceeded 20° in the TKA group and 15° in the controls. Reported perceived exertion was only significantly increased by FC in the control group (control P<0.001, TKA P=0.058). Simulated knee FCs less than 20° do not increase Cw or perceived exertion in TKA recipients.

kmth price sealed-bid auctions with general independent values and equilibrium linear mark-ups

A generalised bidding model is developed to calculate a bidder’s expected profit and auctioners expected revenue/payment for both a General Independent Value and Independent Private Value (IPV) kmth price sealed-bid auction (where the mth bidder wins at the kth bid payment) using a linear (affine) mark-up function. The Common Value (CV) assumption, and highbid and lowbid symmetric and asymmetric First Price Auctions and Second Price Auctions are included as special cases. The optimal n bidder symmetric analytical results are then provided for the uniform IPV and CV models in equilibrium. Final comments concern implications, the assumptions involved and prospects for further research.

Managing climate change ‘by the numbers’ in a UK energy company

This paper examines how ideas and practices of accounting come together in turning the abstract concept of climate change into a new non-financial performance measure in a large energy company in the UK. It develops the notion of ‘governmental management’ to explain how the firm’s carbon dioxide emissions were transformed into a new organisational object that could be made quantifiable, measureable and ultimately manageable because of the modern power of accounting in tying disciplinary subjectivities and objectivities together whilst operating simultaneously at the level of individual and the organisation. Examining these interrelations highlights the constitutive nature of accounting in creating not just new categories for accounting’s attention, but in turn new organisational knowledge and knowledge experts in the making up accounting for climate change. Significantly, it appears these new knowledge experts are no longer accountants: which may help explain accounting’s evolution into evermore spheres of influence as we increasingly choose to manage our world ‘by the numbers’.

Privacy in the new era of visible and sharable energy-use information

A new era of visible and sharable electricity information is emerging. Where eco-feedback is installed, households can now visualise many aspects of their energy consumption and share this information with others through Internet platforms such as social media. Despite providing users with many affordances, eco-feedback information can make public previously private actions from within the intimate setting of the family home. This paper represents a study focussing specifically on the privacy aspects of nascent ways for viewing and sharing this new stream of personal information. It explores the nuances of privacy related to eco-feedback both within and beyond the family home. While electricity consumption information may not be considered private itself, the household practices which eco-feedback systems makes visible may be private. We show that breaches of privacy can occur in unexpected ways and have the potential to cause distress. The paper concludes with some suggestions for how to realise the benefits of sharing energy consumption information whist effectively maintaining individuals’ conceptions of adequate privacy.

A model predictive approach for community battery energy storage system optimization

This paper presents an efficient algorithm for optimizing the operation of battery storage in a low voltage distribution network with a high penetration of PV generation. A predictive control solution is presented that uses wavelet neural networks to predict the load and PV generation at hourly intervals for twelve hours into the future. The load and generation forecast, and the previous twelve hours of load and generation history, is used to assemble load profile. A diurnal charging profile can be compactly represented by a vector of Fourier coefficients allowing a direct search optimization algorithm to be applied. The optimal profile is updated hourly allowing the state of charge profile to respond to changing forecasts in load.

Building energy simulation and parallel computing : opportunities and challenges

Increased focus on energy cost savings and carbon footprint reduction efforts improved the visibility of building energy simulation, which became a mandatory requirement of several building rating systems. Despite developments in building energy simulation algorithms and user interfaces, there are some major challenges associated with building energy simulation; an important one is the computational demands and processing time. In this paper, we analyze the opportunities and challenges associated with this topic while executing a set of 275 parametric energy models simultaneously in EnergyPlus using a High Performance Computing (HPC) cluster. Successful parallel computing implementation of building energy simulations will not only improve the time necessary to get the results and enable scenario development for different design considerations, but also might enable Dynamic-Building Information Modeling (BIM) integration and near real-time decision-making. This paper concludes with the discussions on future directions and opportunities associated with building energy modeling simulations.

Methods to improve the energy output of dye sensitized solar cell arrays

The work examined the operation and optimisation of dye-sensitised solar cell arrays, informing ways to improve performance through materials choices and geometrical design. Methods to improve the output of solar arrays under shading by external objects like trees or building were developed.

Approximation of Bayesian predictive p-values with regression ABC

In the Bayesian framework a standard approach to model criticism is to compare some function of the observed data to a reference predictive distribution. The result of the comparison can be summarized in the form of a p-value, and it's well known that computation of some kinds of Bayesian predictive p-values can be challenging. The use of regression adjustment approximate Bayesian computation (ABC) methods is explored for this task. Two problems are considered. The first is the calibration of posterior predictive p-values so that they are uniformly distributed under some reference distribution for the data. Computation is difficult because the calibration process requires repeated approximation of the posterior for different data sets under the reference distribution. The second problem considered is approximation of distributions of prior predictive p-values for the purpose of choosing weakly informative priors in the case where the model checking statistic is expensive to compute. Here the computation is difficult because of the need to repeatedly sample from a prior predictive distribution for different values of a prior hyperparameter. In both these problems we argue that high accuracy in the computations is not required, which makes fast approximations such as regression adjustment ABC very useful. We illustrate our methods with several samples.

Solar Assisted Fast Pyrolysis: A Novel Approach of Renewable Energy Production

Biofuel produced by fast pyrolysis from biomass is a promising candidate. The heart of the system is a reactor which is directly or indirectly heated to approximately 500°C by exhaust gases from a combustor that burns pyrolysis gas and some of the by-product char. In most of the cases, external biomass heater is used as heating source of the system while internal electrical heating is recently implemented as source of reactor heating. However, this heating system causes biomass or other conventional forms of fuel consumption to produce renewable energy and contributes to environmental pollution. In order to overcome these, the feasibility of incorporating solar energy with fast pyrolysis has been investigated. The main advantages of solar reactor heating include renewable source of energy, comparatively simpler devices, and no environmental pollution. A lab scale pyrolysis setup has been examined along with 1.2 m diameter parabolic reflector concentrator that provides hot exhaust gas up to 162°C. The study shows that about 32.4% carbon dioxide (CO2) emissions and almost one-third portion of fuel cost are reduced by incorporating solar heating system. Successful implementation of this proposed solar assisted pyrolysis would open a prospective window of renewable energy.

Battery energy storage systems to improve power system frequency response

Battery energy storage systems (BESS) are becoming feasible to provide system frequency support due to recent developments in technologies and plummeting cost. Adequate response of these devices becomes critical as the penetration of the renewable energy sources increases in the power system. This paper proposes effective use of BESS to improve system frequency performance. The optimal capacity and the operation scheme of BESS for frequency regulation are obtained using two staged optimization process. Furthermore, the effectiveness of BESS for improving the system frequency response is verified using dynamic simulations.