A new energy efficient cluster-head and backup selection scheme in WSN

Despite significant advancements in wireless sensor networks (WSNs), energy conservation remains one of the most important research challenges. Proper organization of nodes (clustering) is one of the major techniques to expand the lifespan of the whole network through aggregating data at the cluster head. The cluster head is the backbone of the entire cluster. That means if a cluster head fails to accomplish its function, the received and collected data by cluster head can be lost. Moreover, the energy consumption following direct communications from sources to base stations will be increased. In this paper, we propose a type-2 fuzzy based self-configurable cluster head selection (SCCH) approach to not only consider the selection criterion of the cluster head but also present the cluster backup approach. Thus, in case of cluster failure, the system still works in an efficient way. The novelty of this protocol is the ability of handling communication uncertainty, which is an inherent operational aspect of sensor networks. The experiment results indicate SCCH performs better than other recently developed methods.

Analysing building energy use using sub metering and external weather data

The Intergovernmental Panel on Climate Change and the McKinsey Greenhouse Gas abatement studies have highlighted reduction of building energy consumption as a primary cost-effective element in the abatement of Global Warming. Nevertheless, the energy investigation in most of our existing building stock remains at a novice level at best. Building sub-metering, by which we mean any secondary, hourly, metering (after the main) of various circuits, provides substantial information on when and where energy is used in specific buildings. Furthermore, combining this information with external weather data provides information beyond basic metering results. This paper discusses three case studies and explains how sub-metering, augmented by external solar and temperature data, benefits energy management and identified problems. It explains how different methods of analysing energy usage allowed: justifiable sizing of a solar photovoltaic system, with a calculated Cooling Degree Unit, identified the absence of savings from a proprietary chiller controller, and the energy variation due to user schedules and external conditions indicated anomalies in energy use. The advantages of wireless access are noted. Extracting information in graphical formats suggests better strategies to understand and control energy use.

Key influences on office comfort and energy performance in different climates

This paper investigates the magnitude of influence of climate, architectural design and occupants on thermal comfort and final energy consumption in offices in different climates. A parametric study for a typical cellular office room has been conducted using the simulation software EnergyPlus. Two different occupant scenarios are each compared with three different architectural design variations and modelled in the context of three different locations for the IPCC climate change scenario A2 for 2030. The parameters evaluated in this study are final energy consumption and adaptive thermal comfort according to ASHRAE Standard 55. The study shows that the impact of occupants on final energy performance is larger than the impact of architectural design in all investigated climates, but the impact of architectural design is predominant concerning thermal comfort. Warmer climates show larger optimisation potential for comfort and energy performance in offices compared to colder climates.

Integration of resident's energy costs in short stay accommodation billing system

This paper presents an energy management software package and the supporting energy information automation system to enable the short stay accommodation industry to determine actual residents' energy consumption. The proposed energy management system is used to develop a 'novel deal' method in short stay accommodation based on integration of the room level energy costs into the billing system. This system has the potential to increase energy efficiency in the short stay accommodation industry by actively engaging the residents in the energy saving process. The system is economically and environmentally sustainable and can achieve a high level of energy efficiency with low expenses. The proposed model is also effective for carbon mitigation, and offers a value proposition for all the parties in the short stay accommodation industry, including owners and residents. The paper presents a software system to capture room level energy consumption information and to present the detail of consumed energy information in the resident's accommodation bill. The presented information includes energy consumption profile, the greenhouse gas emission profile, and the cost associated to the energy related services.

A data mining framework for electricity consumption analysis from meter data

This paper presents a novel data mining framework for the exploration and extraction of actionable knowledge from data generated by electricity meters. Although a rich source of information for energy consumption analysis, electricity meters produce a voluminous, fast-paced, transient stream of data that conventional approaches are unable to address entirely. In order to overcome these issues, it is important for a data mining framework to incorporate functionality for interim summarization and incremental analysis using intelligent techniques. The proposed Incremental Summarization and Pattern Characterization (ISPC) framework demonstrates this capability. Stream data is structured in a data warehouse based on key dimensions enabling rapid interim summarization. Independently, the IPCL algorithm incrementally characterizes patterns in stream data and correlates these across time. Eventually, characterized patterns are consolidated with interim summarization to facilitate an overall analysis and prediction of energy consumption trends. Results of experiments conducted using the actual data from electricity meters confirm applicability of the ISPC framework.

Applied econometrics and implications for energy economics research

Developments in applied econometrics, particularly with regard to unit root tests and cointegration tests, have motivated a rich empirical literature on energy economics over the last decade. This study reviews recent developments in time series econometrics applications in the energy economics literature. We first consider the literature on the integration properties of energy variables. We begin with a discussion of the implications of whether energy variables contain a unit root and proceed to examine how results differ according to the specific unit root or stationarity test employed. We then proceed to examine recent developments in the literature on cointegration, Granger causality and long-run estimates between (disaggregated) energy consumption and economic growth. We review both single country and panel studies and pay particular attention to studies which have expanded the literature through adding variables such as financial development and trade, in addition to energy consumption to the augmented production function, as well as studies which have extended the literature through examining disaggregated energy consumption by type. In each case we highlight best practice in the literature, point to limitations in the literature, including econometric modeling challenges, and suggest recommendations for future research. A key message of our survey is that the profession needs to guard against 'overload' of research in these areas as most applied studies are no longer adding anything more to what is already known. © 2014 Elsevier B.V. All rights reserved.

Energy performance of medium-sized healthcare buildings in Victoria, Australia- a case study

This paper investigates the energy performance of three medium-sized healthcare buildings in Victoria, Australia, that operate only during the daytime. The aim is to provide preliminary understanding of energy consumption in this particular typology in Australia in relation to the available benchmarks. This paper also identifies the differences of energy consumption between different functional areas within medium health facilities. Building features and operational characteristics contributing to the variations in healthcare energy performance are discussed. The total annual energy consumption data ranging from 167-306 kWh/m(2) or 42-72 kWh/m(3) were compared against international data from various climatic zones. Some of the drivers of energy consumption were determined and potentials for energy and water conservation were identified. Comparison with international standards shows a possibility to achieve lower energy consumption in Victorian healthcare buildings.

An investigation for improved home energy management

 Increasing household energy consumption and increasing primary energy cost urged to improve home energy efficiency. Improved energy management can suggest the ways to improve home energy efficiency. Various home appliances are the prime cause to the increased power demand. Appliance's energy rating information helps to develop awareness and reduce energy consumption. Load shifting can help to reduce overall cost of used energy bill by shifting peak time load to off-peak time. However most of the present appliances remains in standby mode (active or passive) for a significant part of the day, and load shifting cannot reduce the total energy consumption. Therefore investigation is required to identify any possible scopes to improve energy management at home. This paper investigated several home appliances and monitored daily time of use power consumption. It was found that by controlling standby power from a daily home load of 4.482 kWh, power demand can be reduced 12.56% moreover energy related greenhouse gas (GHG) emission can be reduced 133.08kg/year.

Energy efficient information discovery approach for range queries in multi-dimensional WSNs

Multidimensional WSNs are deployed in complex environments to sense and collect data relating to multiple attributes (multi-dimensional data). Such networks present unique challenges to data dissemination, data storage and in-network query processing (information discovery). Recent algorithms proposed for such WSNs are aimed at achieving better energy efficiency and minimizing latency. This creates a partitioned network area due to the overuse of certain nodes in areas which are on the shortest or closest or path to the base station or data aggregation points which results in hotspots nodes. In this paper, we propose a time-based multi-dimensional, multi-resolution storage approach for range queries that balances the energy consumption by balancing the traffic load as uniformly as possible. Thus ensuring a maximum network lifetime. We present simulation results to show that the proposed approach to information discovery offers significant improvements on information discovery latency compared with current approaches. In addition, the results prove that the Quality of Service (QoS) improvements reduces hotspots thus resulting in significant network-wide energy saving and an increased network lifetime.

On the use of panel cointegration tests in energy economics

There is a burgeoning literature based on using panel cointegration techniques to study the relationship between energy consumption and GDP. Most panel cointegration tests employed take no cointegration as the null hypothesis. The current paper illustrates how a rejection by such a test cannot be taken as evidence of cointegration for the panel as a whole, a fact that seems to have gone largely unnoticed in the literature. Hence, even if the no cointegration null is rejected, this evidence is not enough to ensure that the relationship can be meaningfully estimated, as most (if not all) estimators in the literature require that the panel is cointegrated as a whole.

Energy productivity and total-factor productivity in the Australian construction industry

There is urgent need to consider energy consumption when measuring total-factor productivity in the construction industry. This paper adopts the Malmquist index method to investigate the factors affecting the energy productivity of the Australian construction industry and compares them with those decomposed from the total-factor productivity. An input-oriented distance function and a contemporaneous benchmark technology are employed to develop the data envelopment analysis models. The Malmquist productivity index is decomposed into the technological change, pure technical efficiency change and activity effect to gain comprehensive insights into changes of construction productivity in the Australian states and territories over the past two decades. Research results show that both energy productivity and total-factor productivity improved in Australia, particularly related to technological development. The pure technical efficiency and activity indices changed slightly over time or across regions. This study demonstrates that there exists a linkage between energy productivity and total-factor productivity through their technological and technical efficiency changes. The Australian construction industry could enhance these two productivities by introducing advanced technologies and implementing them efficiently.

Stochastic optimization models for energy management in carbonization process of carbon fiber production

Industrial producers face the task of optimizing production process in an attempt to achieve the desired quality such as mechanical properties with the lowest energy consumption. In industrial carbon fiber production, the fibers are processed in bundles containing (batches) several thousand filaments and consequently the energy optimization will be a stochastic process as it involves uncertainty, imprecision or randomness. This paper presents a stochastic optimization model to reduce energy consumption a given range of desired mechanical properties. Several processing condition sets are developed and for each set of conditions, 50 samples of fiber are analyzed for their tensile strength and modulus. The energy consumption during production of the samples is carefully monitored on the processing equipment. Then, five standard distribution functions are examined to determine those which can best describe the distribution of mechanical properties of filaments. To verify the distribution goodness of fit and correlation statistics, the Kolmogorov-Smirnov test is used. In order to estimate the selected distribution (Weibull) parameters, the maximum likelihood, least square and genetic algorithm methods are compared. An array of factors including the sample size, the confidence level, and relative error of estimated parameters are used for evaluating the tensile strength and modulus properties. The energy consumption and N2 gas cost are modeled by Convex Hull method. Finally, in order to optimize the carbon fiber production quality and its energy consumption and total cost, mixed integer linear programming is utilized. The results show that using the stochastic optimization models, we are able to predict the production quality in a given range and minimize the energy consumption of its industrial process.

Fast and energy efficient data storage for information discovery in multi-dimensional WSNs

Multidimensional WSNs are deployed in complex environments to sense and collect data relating to multiple attributes (multi-dimensional data). An efficient information dis-covery for multi-dimensional WSNs deployed in mission–critical environments has become an essential research consideration. Timely and energy efficient information discovery is very impor-tant to maintain the QoS of such mission critical applications. An inefficient information discovery mechanism will result in high transmission of data packets over the network creating bottlenecks leading to unbalanced energy consumption over the network. High latency and inefficient energy consumption will have a direct effect on the QoS of mission-critical applications of particular importance in this regard is the minimization of hotspots.