This paper proposes a real-time predictive energy management strategy (PEMS) of plug-in hybrid electric vehicles for coordination control of fuel economy and battery lifetime, including velocity ...predictor, state-of-charge (SOC) reference generator, and online optimization. In velocity predictor, the radial basis function neural network algorithm is adopted to accurately estimate the future drive velocity. Based on predictive velocity and current driven distance, the SOC reference in predictive horizon can be determined online by reference generator. To coordinate fuel consumption and battery degradation, a model predictive control problem of cost minimization including fuel consumption cost, electricity cost of battery charging/discharging, and equivalent cost of battery degradation, is formulated. To mitigate the huge calculation burden in optimization, the continuation/generalized minimal residual (C/GMRES) algorithm is delegated to find the expected engine power command in real time. Since original C/GMRES algorithm cannot directly handle inequality constraints, the external penalty method is employed to meet physical inequality limits of powertrain. Numerical simulations are carried out and yield the desirable performance of the proposed PEMS in fuel consumption minimization and battery aging restriction. More importantly, the proposed C/GMRES algorithm shows great solving quality and real-time applicability in PEMS by comparing with sequence quadratic programming and genetic algorithms.
•Battery degradation is considered in predictive energy management.•RBF-NN algorithm is adopted to accurately predict future velocity.•SOC reference in predictive horizon is obtained by a modified method.•C/GMRES algorithm is applied to achieve real-time predictive energy management.•Performance and computational efficiency by proposed strategy are verified.
Poorly implemented energy subsidies are economically costly to taxpayers and damage the environment. This report aims at providing the emerging lessons form a representative sample of case studies in ...20 developing countries that could help policy makers to address implementation challenges, including overcoming political economy and affordability constraints. The sample has selected on the basis of a number of criteria, including the countrys level of development (and consumption), developing country region, energy security and the fuel it subsidies (petroleum fuel, electricity, natural gas). The case studies were supported by data collection related to direct budgetary subsidies, fuel and electricity tariffs, and household survey data.The analysis provides strong evidence of the success of reforms in reducing the associated fiscal burden. For the sample of countries, the average energy subsidy recorded in the budget was reduced from 1.8% in 2004 to 1.3%GDP in 2010. The reduction of subsidies is particularly remarkable for net energy importers. Pass-through of international fuel prices was also notable in the case of electricity generated by fossil fuel. For the sample of countries, the average end-user electricity tariff increased by 50%, from USD 6 cents in 2002 to USD 9 cents per kWh in 2010.In spite of the relatively price inelastic demand for gasoline and diesel, fossil fuel consumption in the road sector (per unit of GDP) declined in the 20 countries examined from 53 (44) in 2002 to about 23 kt oil equivalent per million of GDP in 2008 in the case of gasoline (Diesel). The most notable decline in consumption was recorded in the low and lower middle income countries. This reflects the much higher rate of growth in GDP in this group of countries and underlines the opportunities to influence future consumption behavior rather than modifying
the existing consumption patterns, overcoming inertia and vested interests. Similar trends are recorded for power consumption.While there is no one-size-fits-all model for subsidy reform, implementation of compensatory social policies and an effective communication strategy, before the changes are introduced, reduces helped with the implementation of reforms.
•Energy consumption patterns classification was applied to building energy prediction.•Four energy consumption patterns were classified according to the analysis of decision tree.•Ensemble learning ...models were established to predict hourly energy consumption based on four patterns.•The value of proposed method was evaluated in different training cases.
Accurate building energy consumption prediction plays an important role in building energy management and energy policy. However, traditional prediction methods of building energy consumption fail to consider the running conditions of buildings in different periods, which results in the failure of best forecasting effect. This study presents a prediction strategy of building energy consumption based on ensemble learning and energy consumption patternclassification. Hourly meteorological data from a meteorological station and energy consumption data from an office building in New York City are used for this work. First, decision tree is employed to mining energy consumption patterns and classify energy consumption data into corresponding categories. Then, the ensemble learning method is employed to establish energy consumption prediction models for each pattern. Finally, the prediction accuracy of the proposed method is compared with other three methods, i.e., ensemble learning without energy consumption pattern classification, SVR and ANN. Also, the robustness of various methods is investigated by comparing their prediction performance under different training data amounts. Results show that there are four classified energy consumption patterns of the building and significant differences among them. The ensemble learning model with energy consumption pattern classification achieves the best prediction with 17.7%, 16.1%, 15.4%, 15.8%, 15.6% of CVRMSE under 20%, 40%, 60%, 80% and 100% data availability, respectively. It illustrates that the proposed strategy is reliable and effective. Additionally, this strategy can obtain acceptable performance with less training data, which is helpful to the application of energy consumption prediction.
Commodity Activism Mukherjee, Roopali; Banet-Weiser, Sarah
02/2012, Volume:
21
eBook
Buying (RED) products - from Gap T-shirts to Apple--to fight AIDS. Drinking a Caring Cup of coffee at the Coffee Bean and Tea Leaf to support fair trade. Driving a Toyota Prius to fight global ...warming. All these commonplace activities point to a central feature of contemporary culture: the most common way we participate in social activism is by buying something. Roopali Mukherjee and Sarah Banet-Weiser have gathered an exemplary group of scholars to explore this new landscape through a series of case studies of commodity activism. Drawing from television, film, consumer activist campaigns, and cultures of celebrity and corporate patronage, the essays take up examples such as the Dove Real Beauty campaign, sex positive retail activism, ABC's Extreme Home Makeover, and Angelina Jolie as multinational celebrity missionary. Exploring the complexities embedded in contemporary political activism, Commodity Activism reveals the workings of power and resistance as well as citizenship and subjectivity in the neoliberal era. Refusing to simply position politics in opposition to consumerism, this collection teases out the relationships between material cultures and political subjectivities, arguing that activism may itself be transforming into a branded commodity.
To examine the association between red/processed meat consumption and glycaemic conditions (i.e. prediabetes (preDM) and diabetes mellitus (DM)) among middle-aged residents in rural Khánh Hòa, ...Vietnam.
In this cross-sectional study, a multinomial logistic regression model was used to examine the association between daily consumption of red/processed meat (0-99 g, 100-199 g or ≥ 200 g) and preDM/DM with adjustments for socio-demographic, lifestyle-related and health-related variables.
Khánh Hòa Province, Vietnam.
The study used data collected through a baseline survey conducted during a prospective cohort study on CVD among 3000 residents, aged 40-60 years, living in rural communes in Khánh Hòa Province.
The multinomial regression model revealed that the relative-risk ratios for DM were 1·00 (reference), 1·11 (95 % CI = 0·75, 1·62) and 1·80 (95 % CI = 1·40, 2·32) from the lowest to the highest red/processed meat consumption categories (
= 0·006). The corresponding values for preDM were 1·00 (reference), 1·25 (95 % CI = 1·01, 1·54) and 1·67 (95 % CI = 1·20, 2·33) (
= 0·004). We did not find any evidence of statistical significance in relation to poultry consumption.
Increased red/processed meat consumption, but not poultry consumption, was positively associated with the prevalence of preDM/DM in rural communes in Khánh Hòa Province, Vietnam. Dietary recommendations involving a reduction in red/processed meat consumption should be considered in low- and middle-income countries.
The Passenger Cars Corporate Average Fuel Consumption and New Energy Vehicle Credit Regulation (dual-credit policy) was enacted by the Chinese government in 2017 to stimulate the fuel-efficient and ...electrification technologies in the China's passenger vehicle market. This study summarizes the dual-credit policy and develops the New Energy and Oil Consumption Credits Model to quantify the impacts of this policy on consumer choices and industry profits, where internal subsidies as decision variables are used to represent industry responses to the policy. Scenarios in 2016–2020 are simulated and discussed. Key findings from the model results include: (1) the Corporate Average Fuel Consumption rules alone may stimulate more plug-in electric vehicle (PEV) sales than the dual-credit policy; however, (2) the dual-credit policy could stimulate more battery electric vehicles (BEVs) in market, compared to other policy scenarios; (3) the industry could “lose” approximately $2122/vehicle by 2020 under the dual-credit policy; (4) battery electric sedans with a range greater than 250 km and plug-in hybrid SUVs could be popular under the dual-credit policy; (5) credit allocations for BEVs in the dual-credit policy can influence the PEV production; and (6) reduction of the fuel-efficient technology costs helps to minimize profit losses impacted by the policy.
•A quantitative policy analysis model is built for China's vehicle industry.•The dual-credit policy is compared with other policies in scenarios analysis.•BEV-350 sedan will be the most popular under the dual-credit policy.•Industry might lose $2122/vehicle in 2020 under the policy in NEOCC.•Reduction of the fuel-efficient technology cost can mitigate industry profit loss.
Minimalism is an increasingly popular lifestyle movement in western economies (predominantly in the USA, Japan and Europe) that involves voluntarily reducing consumption and limiting one’s ...possessions to a bare minimum. This is with the intention of making space for the ‘important’ (potentially immaterial) things that are seen to add meaning and value to one’s life. Drawing on interviews with minimalists in the UK, this article reveals that minimalists practice sustainable (non)consumption via limiting their consumption. This is achieved by actively buying less, using up and maintaining what is owned, and, when objects are acquired, only practising highly intentional, considered and (sometimes) ethical consumption. For some, such practices are predominantly based on strong ethical and environmental motivations or are seen as a positive ‘by-product’ of their minimalist lifestyles. Whilst for others, their motivations are primarily aligned to personal well-being. The article subsequently argues that the limited and considered practices of minimalist consumption can be seen as sustainable practices in outcome, if not always in intent.
In public debates, communication campaigns and public policies, it is increasingly common to attribute to consumers and their agency an ability to help solve a broad array of societal problems. This ...tendency is particularly clear in the field of food consumption, owing to the fact that food is both materially and symbolically central for consumers in everyday life as well as for large scale institutionalized dynamics. In order to shed light on the challenges facing food consumption, this volume takes an innovative theoretical approach, presenting four empirical Danish case studies which are compared with other analyses drawn from the wider international context. Consumption Challenged will appeal not only to sociologists of consumption, risk and the environment, but also to policy makers and researchers in the fields of geography, communication, media, governance and social psychology.
PurposeEthical consumption is an integral component for the sustainable development in the world and is especially challenging in the Western consumer society. This research demonstrates that ...mindfulness, a Buddhism-based notion, is associated with two related and distinctive approaches of ethical consumption: refinement and reduction. It examines the psychological mechanisms underlying the effects of mindfulness on these two approaches of ethical consumption.Design/methodology/approachSelf-report data were collected through an online survey with consumers from western societies (N = 523).FindingsThe findings show (1) that the significance of mindfulness on both approaches of ethical consumption and (2) that the contrast between the different mechanisms underlying them. Specifically, the mindfulness–consumption refinement link is fully mediated by connectedness-to-nature whereas the mindfulness–consumption reduction link is fully mediated by connectedness-to-nature and self-control. A series of supplementary studies further confirmed the proposed model.Research limitations/implicationsIt demonstrates the multifaceted and complex nature of ethical consumption, which is positively associated with mindfulness but through distinctive psychological mechanisms.Practical implicationsThe multifaceted and complex nature of ethical consumption and its underlying drivers need special attention. Mindfulness can be an effective means to boost ethical consumption behavior. Meanwhile, nurturing the sense of connectedness to nature and self-control capability facilitates the path-through of the positive impacts of mindfulnessSocial implicationsThe findings can be adopted to enhance the effectiveness of mindfulness practice in promoting ethical consumption towards achieving the Sustainable Consumption goal, especially in the West.Originality/valueThe paper makes original contribution by conceptualizing two interrelated and distinctive approaches of ethical consumption and shows how mindfulness promotes both through different mediating pathways. Overall, this study paints a clearer picture how mindfulness relates to ethical consumption.
Electric energy forecasting domain attracts researchers due to its key role in saving energy resources, where mainstream existing models are based on Gradient Boosting Regression (GBR), Artificial ...Neural Networks (ANNs), Extreme Learning Machine (ELM) and Support Vector Machine (SVM). These models encounter high-level of non-linearity between input data and output predictions and limited adoptability in real-world scenarios. Meanwhile, energy forecasting domain demands more robustness, higher prediction accuracy and generalization ability for real-world implementation. In this paper, we achieve the mentioned tasks by developing a hybrid sequential learning-based energy forecasting model that employs Convolution Neural Network (CNN) and Gated Recurrent Units (GRU) into a unified framework for accurate energy consumption prediction. The proposed framework has two major phases: (1) data refinement and (2) training, where the data refinement phase applies preprocessing strategies over raw data. In the training phase, CNN features are extracted from input dataset and fed in to GRU, that is selected as optimal and observed to have enhanced sequence learning abilities after extensive experiments. The proposed model is an effective alternative to the previous hybrid models in terms of computational complexity as well prediction accuracy, due to the representative features' extraction potentials of CNNs and effectual gated structure of multi-layered GRU. The experimental evaluation over existing energy forecasting datasets reveal the better performance of our method in terms of preciseness and efficiency. The proposed method achieved the smallest error rate on Appliances Energy Prediction (AEP) and Individual Household Electric Power Consumption (IHEPC) datasets, when compared to other baseline models.
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