Embodied energy assessment of building materials in India using process and input-output analysis

Growing demand for urban built spaces has resulted in unprecedented exponential rise in production and consumption of building materials in construction. Production of materials requires significant energy and contributes to pollution and green house gas (GHG) emissions. Efforts aimed at reducing energy consumption and pollution involved with the production of materials fundamentally requires their quantification. Embodied energy (EE) of building materials comprises the total energy expenditure involved in the material production including all upstream processes such as raw material extraction and transportation. The current paper deals with EE of a few common building materials consumed in bulk in Indian construction industry. These values have been assessed based on actual industrial survey data. Current studies on EE of building materials lack agreement primarily with regard to method of assessment and energy supply assumptions (whether expressed in terms of end use energy or primary energy). The current paper examines the suitability of two basic methods; process analysis and input-output method and identifies process analysis as appropriate for EE assessment in the Indian context. A comparison of EE values of building materials in terms of the two energy supply assumptions has also been carried out to investigate the associated discrepancy. The results revealed significant difference in EE of materials whose production involves significant electrical energy expenditure relative to thermal energy use. (C) 2014 Elsevier B.V. All rights reserved.

Economic Evaluation of Energy Efficiency Labelling in Domestic Appliances: the Spanish Market

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A decoupled whole-building simulation engine for rapid exhaustive search of low-carbon and low-energy building refurbishment options

This paper presents the development of a new building physics and energy supply systems simulation platform. It has been adapted from both existing commercial models and empirical works, but designed to provide expedient exhaustive simulation of all salient types of energy- and carbon-reducing retrofit options. These options may include any combination of behavioural measures, building fabric and equipment upgrades, improved HVAC control strategies, or novel low-carbon energy supply technologies. We provide a methodological description of the proposed model, followed by two illustrative case studies of the tool when used to investigate retrofit options of a mixed-use office building and primary school in the UK. It is not the intention of this paper, nor would it be feasible, to provide a complete engineering decomposition of the proposed model, describing all calculation processes in detail. Instead, this paper concentrates on presenting the particular engineering aspects of the model which steer away from conventional practise. © 2011 Elsevier Ltd.

A probabilistic energy model for non-domestic building sectors applied to analysis of school buildings in greater London

The diversity of non-domestic buildings at urban scale poses a number of difficulties to develop models for large scale analysis of the stock. This research proposes a probabilistic, engineering-based, bottom-up model to address these issues. In a recent study we classified London's non-domestic buildings based on the service they provide, such as offices, retail premise, and schools, and proposed the creation of one probabilistic representational model per building type. This paper investigates techniques for the development of such models. The representational model is a statistical surrogate of a dynamic energy simulation (ES) model. We first identify the main parameters affecting energy consumption in a particular building sector/type by using sampling-based global sensitivity analysis methods, and then generate statistical surrogate models of the dynamic ES model within the dominant model parameters. Given a sample of actual energy consumption for that sector, we use the surrogate model to infer the distribution of model parameters by inverse analysis. The inferred distributions of input parameters are able to quantify the relative benefits of alternative energy saving measures on an entire building sector with requisite quantification of uncertainties. Secondary school buildings are used for illustrating the application of this probabilistic method. © 2012 Elsevier B.V. All rights reserved.

Energy harvesting embedded wireless sensor system for building environment applications

For many wireless sensor networks applications, indoor light energy is the only ambient energy source commonly available. Many advantages and constraints co-exist in this technology. However, relatively few indoor light powered harvesters have been presented and much research remains to be carried out on a variety of related design considerations and trade-offs. This work presents a solution using the Tyndall mote and an indoor light powered wireless sensor node. It analyses design considerations on several issues such as indoor light characteristics, solar panel component choice, maximum power point tracking, energy storage elements and the trade-offs and choices between them.

Carbon mitigation costs for the commercial building sector: Discrete-continuous choice analysis of multifuel energy demand

We estimate a carbon mitigation cost curve for the U.S. commercial sector based on econometric estimation of the responsiveness of fuel demand and equipment choices to energy price changes. The model econometrically estimates fuel demand conditional on fuel choice, which is characterized by a multinomial logit model. Separate estimation of end uses (e.g., heating, cooking) using the U.S. Commercial Buildings Energy Consumption Survey allows for exceptionally detailed estimation of price responsiveness disaggregated by end use and fuel type. We then construct aggregate long-run elasticities, by fuel type, through a series of simulations; own-price elasticities range from -0.9 for district heat services to -2.9 for fuel oil. The simulations form the basis of a marginal cost curve for carbon mitigation, which suggests that a price of $20 per ton of carbon would result in an 8% reduction in commercial carbon emissions, and a price of $100 per ton would result in a 28% reduction. © 2008 Elsevier B.V. All rights reserved.

Cities Ready for Energy Crises - Building Urban energy Resilience

Various sources indicate that threats to modern cities lie in the availability of essential streams, among which energy. Most cities are strongly reliant on fossil fuels; not one case of a fully self-sufficient city is known. Engineering resilience is the rate at which a system returns to a single steady or cyclic state following a perturbation. Certain resilience, for the duration of a crisis, would improve the urban capability to survive such a period without drastic measures.
The capability of cities to prepare for and respond to energy crises in the near future is supported by greater or temporary self-sufficiency. The objective of the underlying research is a model for a city – including its surrounding rural area – that can sustain energy crises. Therefore, accurate monitoring of the current urban metabolism is needed for the use of energy. This can be used to pinpoint problem areas. Furthermore, a sustainable energy system is needed, in which the cycle is better closed. This will require a three-stepped approach of energy savings, energy exchange and sustainable energy generation. Essential is the capacity to store energy surpluses for periods of shortage (crises).
The paper discusses the need for resilient cities and the approach to make cities resilient to energy crises.