Non-intrusive load monitoring approaches for disaggregated energy sensing: a survey.

Appliance Load Monitoring (ALM) is essential for energy management solutions, allowing them to obtain appliance-specific energy consumption statistics that can further be used to devise load scheduling strategies for optimal energy utilization. Fine-grained energy monitoring can be achieved by deploying smart power outlets on every device of interest; however it incurs extra hardware cost and installation complexity. Non-Intrusive Load Monitoring (NILM) is an attractive method for energy disaggregation, as it can discern devices from the aggregated data acquired from a single point of measurement. This paper provides a comprehensive overview of NILM system and its associated methods and techniques used for disaggregated energy sensing. We review the state-of-the art load signatures and disaggregation algorithms used for appliance recognition and highlight challenges and future research directions.

Power management and control strategies for efficient operation of a solar power dominated hybrid DC microgrid for remote power applications

In this paper, a hybrid DC microgrid consisting of a diesel generator with a rectifier, a solar photovoltaic (PV) system, and a battery energy storage system is presented in relation to an effective power management strategy and different control techniques are adopted to power electronic interfaces. The solar PV and battery energy storage systems are considered as the main sources of energy sources that supply the load demand on a daily basis whereas the diesel generator is used as a backup for the emergency operation of the microgrid. All system components are connected to a common DC bus through an appropriate power electronics devices (e.g., rectifier systems, DC/DC converter). Also a detailed sizing philosophy of all components along with the energy management strategy is proposed. Energy distribution pattern of each individual component has been conducted based on the monthly basis along with a power management algorithm. The power delivered by the solar PV system and diesel generator is controlled via DC-DC converterand excitation controllers which are designed based on a linearquadratic regulator (LQR) technique as as proportional integral (PI)controllers. The component level power distribution is investigatedusing these controllers under fluctuating load and solar irradiationconditions and comparative results are presented.

Simulation-based support tool for operator performance in batch manufacturing

In industry, the workload and utilization of shop floor operators is often misunderstood. In this paper, we will present several real case studies, using Discrete Event Simulation (DES) models, which allow us to better understand operators in a batch manufacturing environment. The first study investigates labour in a machining plant consisting of multiple identical CNC machines that batch produce parts. The second study investigates labour in an eight station, gravity die casting rotary table. The results from these studies have shown that there can be potential improvements made by the production planners in the current labour configuration. In the first case study, a matrix is produced that estimates what the operator's utilization levels will be for various configurations. From this, the preferred operator to machine ratio over a range of cycle times is presented. In the second study, the results have shown that by reducing the casting cycle time, the operator would be overloaded. A discrete event simulation of these two cases highlighted areas that were misunderstood by plant management, and provided them with a useful decision support tool for production planning.

Examining the living with possums policy in Victoria, Australia : community knowledge, support and compliance

Studying the human dimensions of wildlife management issues is now considered to be an essential component
of wildlife research. This study examined the Living with Possums policy in Victoria, Australia, in terms of the policy’s
success in educating the community and ensuring community compliance. Postal surveys and telephone interviews
were conducted across three samples from Greater Melbourne. These samples included people who had experiences
with possums on their property (n = 340), veterinary clinics (n = 45) and the general public (n = 103). Significant
levels of non-compliance were uncovered, highlighting the need for a renewed public education campaign to take place
along with a continued interest in this issue from government agencies and councils. The study also revealed
discrepancies between the policy and public preferences for possum management, suggesting that a shift in the
recommended management technique may be warranted.

Office building characteristics and the links with carbon emissions

Purpose – The purpose of this paper is to present research which analysed energy consumption in the Melbourne central business district (CBD) office stock and examined all buildings to identify CO2 emissions in 2005. The rationale was that, by profiling a large group of buildings, it would be possible to identify characteristics of the stock. For example, do older buildings typically emit more CO2 per square metre than newer buildings?
Design/methodology/approach – This research conducted a detailed analysis of all Melbourne CBD office stock to identify which patterns and trends emerged regarding building characteristics and carbon emissions. The study examined variables such as building size, number of employees, occupancy levels, physical characteristics and building age.
Findings – By examining all office stock and aggregating data, the results confirm that it is possible to identify general physical building characteristics and carbon emissions. This research confirmed that clear relationships existed within the Melbourne CBD office stock in terms of building size, age and the density of occupation in relation to CO2 emissions.
Originality/value – Practitioners can apply this knowledge to the professional advice they give to clients to assist in achieving increased energy efficiency in the office stock, for example in refurbishment being conscious that smaller buildings will be generally less energy-efficient than larger ones.

Hybrid prediction method for solar power using different computational intelligence algorithms

Computational Intelligence (CI) holds the key to the development of smart grid to overcome the challenges of planning and optimization through accurate prediction of Renewable Energy Sources (RES). This paper presents an architectural framework for the construction of hybrid intelligent predictor for solar power. This research investigates the applicabil- ity of heterogeneous regression algorithms for 6 hour ahead solar power availability forecasting using historical data from Rockhampton, Australia. Real life solar radiation data is collected across six years with hourly resolution from 2005 to 2010. We observe that the hybrid prediction method is suitable for a reliable smart grid energy management. Prediction reliability of the proposed hybrid prediction method is carried out in terms of prediction error performance based on statistical and graphical methods. The experimental results show that the proposed hybrid method achieved acceptable prediction accuracy. This potential hybrid model is applicable as a local predictor for any proposed hybrid method in real life application for 6 hours in advance prediction to ensure constant solar power supply in the smart grid operation.

On optimally reducing power loss in micro-grids with power storage devices

Smart micro-grids can produce 'renewable' energy and store them in power storage devices. Power loss, however, is a significant problem in power exchange among the micro-grids and between the macro-station and individual micro-grids. To optimally reduce the total power losses in such a power grid system, in this paper, a greedy coalition formation algorithm is proposed, which allows the macro-station to coordinate mutual power exchange among the micro-grids and between each micro-grid and the macro-station. Our algorithm optimizes the total power losses across the entire power grid, including the cost of charging and discharging power storage devices and power losses due to power transfers. The algorithm creates exchange pairs among the micro-grids, giving priority to pairs with higher power loss reduction per exchanged power unit. Through computer-based simulations, we demonstrate that the proposed approach significantly reduces the average power loss compared with the conventional noncooperative method. The simulations also demonstrate that the communications overhead of our proposal (due to negotiations aimed at forming coalitions) does not significantly affect the available communication resource. © 2014 IEEE.

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Electrical load forecasting plays a vital role in order to achieve the concept of next generation power system such as smart grid, efficient energy management and better power system planning. As a result, high forecast accuracy is required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment of power grid. Artificial Intelligence (AI) based techniques are being developed and deployed worldwide in on Varity of applications, because of its superior capability to handle the complex input and output relationship. This paper provides the comprehensive and systematic literature review of Artificial Intelligence based short term load forecasting techniques. The major objective of this study is to review, identify, evaluate and analyze the performance of Artificial Intelligence (AI) based load forecast models and research gaps. The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs. Published literature presented in this paper show the potential of AI techniques for effective load forecasting in order to achieve the concept of smart grid and buildings.

Viable approaches for increasing the efficiency of buiding integrated photovoltaic systems

Nowadays, there is growing interest towards the area of building integrated photovoltaic (BIPV) systems and PV microgrids (MGs) in the field of power generation and distribution systems. This is mainly due to the higher adaptability and compatibility of these systems with preplanned sustainable development strategies in the most urban areas. The quiet operating process and movement free characteristic of photovoltaic systems brought them to the roof tops of the buildings in urban and rural areas and made them the most demanded means of renewable energy systems. This study highlights the problems affecting the performance and efficiency of BIPV systems and presents miscellaneous solutions and recommendations to solve these problems.

Distributed multi-agent scheme to improve dynamic voltage stability of distribution networks

In this paper, a distributed multi-agent scheme is presented for reactive power management with renewable energy sources (RESs). The multi-agent system (MAS) framework is developed for distribution systems to improve the stability which is mostly dominated by voltage and the agents in this framework coordinate among themselves using online information and energy flow. In this paper, the agents basically perform two tasks- reactive power estimation and necessary control actions. The topology of distribution network is used to estimate the required reactive power for maintaining voltage stability where distributed static synchronous compensators (DSTATCOMs) are used to supply this reactive power. The DSTATCOM is controlled by using a linear quadratic regulator (LQR) controller within the agent framework. The proposed scheme is further compared with the conventional approach to validate the simulation results.

AvianBuffer: an interactive tool for characterising and managing wildlife fear responses

The characterisation and management of deleterious processes affecting wildlife are ideally based on sound scientific information. However, relevant information is often absent, or difficult to access or contextualise for specific management purposes. We describe 'AvianBuffer', an interactive online tool enabling the estimation of distances at which Australian birds respond fearfully to humans. Users can input species assemblages and determine a 'separation distance' above which the assemblage is predicted to not flee humans. They can also nominate the diversity they wish to minimise disturbance to, or a specific separation distance to obtain an estimate of the diversity that will remain undisturbed. The dataset is based upon flight-initiation distances (FIDs) from 251 Australian bird species (n = 9190 FIDs) and a range of human-associated stimuli. The tool will be of interest to a wide audience including conservation managers, pest managers, policy makers, land-use planners, education and public outreach officers, animal welfare proponents and wildlife ecologists. We discuss possible applications of the data, including the construction of buffers, development of codes of conduct, environmental impact assessments and public outreach. This tool will help balance the growing need for biodiversity conservation in areas where humans can experience nature. The online resource will be expanded in future iterations to include an international database of FIDs of both avian and non-avian species.

Labour productivity in Iranian construction projects perceptions of chief executive officers

Purpose – The purpose of this paper is twofold. First, it aims to highlight the main factors and items affecting the productivity of construction projects, based on the perceptions of CEOs in construction companies in Iran. Second, the study compares the elicited CEOs’ perceptions against the findings of studies based on the views of such as project managers, middle managers and employees in other levels. Design/methodology/approach – The study drew upon literature on construction work to develop a conceptual model. Further, a total of 60 CEOs from road construction companies were surveyed using a five-point Likert scale questionnaire to generate the data. The collated data were categorised and ranked according to the CEOs perceived level of importance using the relative importance index. Findings – The findings highlight the main factors and items affecting labour productivity in construction projects in Iran as perceived by CEOs, which are mainly of human resources management nature and could be attributed to motivation and managerial policy aspects. The study also recognises that factors associated with the working environment particularly safety and health are perceived as insignificant by Iranian CEOs which could be a concern for the Iranian construction industry. The discussions shed some light on the discrepancies between the perceptions of CEOs and previous studies in regards to major determinants of productivity in the construction context. Originality/value – This study is the first study aiming at discussing the perceptions of CEOs of construction companies active in construction projects in Iran. As such, the study highlights the standpoint of the main decision makers in construction companies in regards to labour productivity in the construction sector. Thus, the key contribution of the present study is providing insight into the perceptions of CEOs, who play the most vital role in strategic development of construction companies whereas previous studies have mostly focused on project or middle managers having a lower influence in determining the strategic plans of companies.

A novel operation and control strategy for a standalone hybrid renewable power system

This paper proposes a novel operation and control strategy for a renewable hybrid power system for a standalone operation. The proposed hybrid system consists of a wind turbine, a fuel cell, an electrolyzer, a battery storage unit, and a set of loads. The overall control strategy is based on a two-level structure. The top level is the energy management and power regulation system. Depending on wind and load conditions, this system generates reference dynamic operating points to low level individual subsystems. The energy management and power regulation system also controls the load scheduling operation during unfavorable wind conditions under inadequate energy storage in order to avoid a system blackout. Based on the reference dynamic operating points of the individual subsystems, the local controllers control the wind turbine, fuel cell, electrolyzer, and battery storage units. The proposed control system is implemented in MATLAB Simpower software and tested for various wind and load conditions. Results are presented and discussed.

Wave hazards on microtidal shore platforms : testing the relationship between morphology and exposure

Open-ocean rocky coasts are dangerous environments when there is a coincidence of recreational activities occurring in areas of high wave energy. Management of drowning fatalities and near-drowning incidents on these landforms is difficult as traditional approaches to beach safety cannot be easily transferred to rocky shores. In this study, we take a morphological approach to quantifying the relative danger of shore platforms in microtidal regions. Platform elevation and nearshore water depth are key variables in determining the likelihood of wave overtopping of the platform edge. The relationship between these variables is tested along a 70-km-long section of the Otway Ranges coast in Victoria, Australia. It is found that exposure is highly variable along short (100 m scale) sections of shore platforms. This variability is driven by the complexity of the nearshore morphology which can have metre-scale relief. As exposed platforms may occur in areas of low wave energy, the morphological exposure index is combined with nearshore wave energy to produce a risk rating. Risk, like exposure, was found to be highly spatially variable. The relationship between elevation and water depth has the potential to provide managers with a tool for assessing safety on rocky shores.