Two-stage capacity optimization approach of multi-energy system considering its optimal operation

With the depletion of fossil fuel and climate change, multi-energy systems have attracted widespread attention in buildings. Multi-energy systems, fuelled by renewable energy, including solar and biomass energy, are gaining increasing adoption in commercial buildings. Most of previous capacity design approaches are formulated based upon conventional operating schedules, which result in inappropriate design capacities and ineffective operating schedules of the multi-energy system. Therefore, a two-stage capacity optimization approach is proposed for the multi-energy system with its optimal operating schedule taken into consideration. To demonstrate the effectiveness of the proposed capacity optimization approach, it is tested on a renewable energy fuelled multi-energy system in a commercial building. The primary energy devices of the multi-energy system consist of biomass gasification-based power generation unit, heat recovery unit, heat exchanger, absorption chiller, electric chiller, biomass boiler, building integrated photovoltaic and photovoltaic thermal hybrid solar collector. The variable efficiency owing to weather condition and part-load operation is also considered. Genetic algorithm is adopted to determine the optimal design capacity and operating capacity of energy devices for the first-stage and second-stage optimization, respectively. The two optimization stages are interrelated; thus, the optimal design and operation of the multi-energy system can be obtained simultaneously and effectively. With the adoption of the proposed novel capacity optimization approach, there is a 14% reduction of year-round biomass consumption compared to one with the conventional capacity design approach

Benchmarks for energy access: Policy vagueness and incoherence as barriers to sustainable electrification of the global south

© 2019 The unavailability of tangible policy benchmarks continues to mitigate against sustainable electrification in the global south. Furthermore, incoherent policy benchmarks as to what should constitute clean energy allow for varying interpretations and divergent options in electrifying households across the global south. The multiplicity of policies to deepen access to improved energy services in the global south notwithstanding, ‘success’ is not in sight until definite and uniform benchmarks guide the roll-out of electrification schemes

Predicting Completion Risk in PPP Projects using Big Data Analytics

Accurate prediction of potential delays in public private partnerships (PPP) projects could provide valuable information relevant for planning and mitigating completion risk in future PPP projects. However, existing techniques for evaluating completion risk remain incapable of identifying hidden patterns in risk behavior within large samples of projects, which are increasingly relevant for accurate prediction. To effectively tackle this problem in PPP projects, this study proposes a Big Data Analytics predictive modeling technique for completion risk prediction. With data from 4294 PPP project samples delivered across Europe between 1992 and 2015, a series of predictive models have been devised and evaluated using linear regression, regression trees, random forest, support vector machine, and deep neural network for completion risk prediction. Results and findings from this study reveal that random forest is an effective technique for predicting delays in PPP projects, with lower average test predicting error than other legacy regression techniques. Research issues relating to model selection, training, and validation are also presented in the study

Comparative study of machine learning-based multi-objective prediction framework for multiple building energy loads

Buildings are one of the significant sources of energy consumption and greenhouse gas emission in urban areas all over the world. Lighting control and building integrated photovoltaic (BIPV) are two effective measures in reducing overall primary energy consumption and carbon emission during building operation. Due to the complex energy nature of the building, accurate day-ahead prediction of heating, cooling, lighting loads and BIPV electrical power production is essential in building energy management. Owing to the changing metrological conditions, diversity and complexity of buildings, building energy load demands and BIPV electrical power production is highly variable. This may lead to poor building energy management, extra primary energy consumption or thermal discomfort. In this study, three machine learning-based multi-objective prediction frameworks are proposed for simultaneous prediction of multiple energy loads. The three machine learning techniques are artificial neural network, support vector regression and long-short-term-memory neural network. Since heating, cooling, lighting loads and BIPV electrical power production share similar affecting factors, it is computational time saving to adopt the proposed multi-objective prediction framework to predict multiple building energy loads and BIPV power production. The ANN-based predictive model results in the smallest mean absolute percentage error while SVM-based one cost the shortest computation time

Insolvency of small civil engineering firms: Critical strategic factors

© 2016 American Society of Civil Engineers. Construction industry insolvency studies have failed to stem the industry's high insolvency tide because many focus on big civil engineering firms (CEF) when over 90% of firms in the industry are small or micro (S&M). This study thus set out to uncover insolvency criteria of S&M CEFs and the underlying factors using mixed methods. Using convenience sampling, the storytelling method was used to execute interviews of 16 respondents from insolvent firms. Narrative and thematic analysis were used to extract 17 criteria under 2 groups. Criteria were used to formulate a questionnaire, of which 81 completed copies were received and analyzed using Cronbach's alpha coefficient and relevance index score for reliability and ranking, respectively. The five most relevant criteria were economic recession, immigration, too many new firms springing up, collecting receivables, and burden of sustainable construction. The four underlying factors established through factor analysis were market forces, competence-based management, operations efficiency and other management issues, and information management. The factors were in line with Mintzberg's and Porter's strategy theories. The results demonstrate that insolvency factors affecting big and small CEF can be quite different and, sometimes, even opposite. This research will provide a unique resource on the factors that should make potential owners of S&M CEF cautious. The criteria are potential variables for insolvency prediction models for S&M CEFs

Designing out construction waste using BIM technology:Stakeholders’ expectations for industry deployment

The need to use Building Information Modelling (BIM) for Construction and Demolition Waste (CDW) minimisation is well documented but most of the existing CDW management tools still lack BIM functionality. This study therefore assesses the expectations of stakeholders on how BIM could be employed for CDW management. After a review of extant literature to assess the limitations of existing CDW management tools, qualitative Focus Group Interviews (FGIs) were conducted with professionals who are familiar with the use of BIM to understand their expectations on the use of BIM for CDW management. The 22 factors identified from the qualitative data analyses were then developed into a questionnaire survey. The exploratory factor analysis of the responses reveals five major groups of BIM expectations for CDW management, which are: (i) BIM-based collaboration for waste management, (ii) waste-driven design process and solutions, (iii) waste analysis throughout building lifecycle, (iv) innovative technologies for waste intelligence and analytics, and (v) improved documentation for waste management. Considering these groups of factors is key to meeting the needs of the stakeholders regarding the use of BIM for CDW management. These groups of factors are important considerations for the implementation and acceptance of BIM-based tools and practices for CDW management within the construction industry.<br/

Reducing waste to landfill: A need for cultural change in the UK construction industry

© 2015 Elsevier Ltd. All rights reserved. Owing to its contribution of largest portion of landfill wastes and consumption of about half of mineral resources excavated from nature, construction industry has been pressed to improve its sustainability. Despite an adoption of several waste management strategies, and introduction of various legislative measures, reducing waste generated by the industry remains challenging. In order to understand cultural factors contributing to waste intensiveness of the industry, as well as those preventing effectiveness of existing waste management strategies, this study examines cultural profile of construction industry. Drawing on four focus group discussions with industry experts, the study employs phenomenological approach to explore waste inducing cultural factors. Combining findings from phenomenological research with extant literatures, the study suggests that in order to reduce waste intensiveness of the construction industry, five waste inducing cultural factors need to be changed. These include (i) "make-do" understanding that usually result in "make-do waste" (ii) non-collaborative culture, which results in reworks and other forms of wasteful activities (iii) blame culture, which encourages shifting of waste preventive responsibilities between designers and contractors, (iv) culture of waste behaviour, which encourages belief in waste inevitability, and (v) conservatism, which hinders diffusion of innovation across the industry. Changing these sets of cultural and behavioural activities is not only important for engendering waste management practices; they are requisite for effectiveness of existing strategies. Improvement in the identified areas is also required for overall improvement and general resource efficiency of the construction industry. Thus, this paper advocates cultural shift as a means of reducing waste landfilled by the construction industry, thereby enhancing sustainability and profitability of the industry

IoT technologies for livestock management: A review of present status, opportunities, and future trends

The world population currently stands at about 7 billion amidst an expected increase in 2030 from 9.4 billion to around 10 billion in 2050. This burgeoning population has continued to influence the upward demand for animal food. Moreover, the management of finite resources such as land, the need to reduce livestock contribution to greenhouse gases, and the need to manage inherent complex, highly contextual, and repetitive day-to-day livestock management (LsM) routines are some examples of challenges to overcome in livestock production. The Internet of Things (IoT)’s usefulness in other vertical industries (OVI) shows that its role will be significant in LsM. This work uses the systematic review methodology of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to guide a review of existing literature on IoT in OVI. The goal is to identify the IoT’s ecosystem, architecture, and its technicalities—present status, opportunities, and expected future trends—regarding its role in LsM. Among identified IoT roles in LsM, the authors found that data will be its main contributor. The traditional approach of reactive data processing will give way to the proactive approach of augmented analytics to provide insights about animal processes. This will undoubtedly free LsM from the drudgery of repetitive tasks with opportunities for improved productivity

Critical factors for insolvency prediction: Towards a theoretical model for the construction industry

© 2016 Informa UK Limited, trading as Taylor & Francis Group. Many construction industry insolvency prediction model (CI-IPM) studies have arbitrarily employed or simply adopted from previous studies different insolvency factors, without justification, leading to poorly performing CI-IPMs. This is due to the absence of a framework for selection of relevant factors. To identify the most important insolvency factors for a high-performance CI-IPM, this study used three approaches. Firstly, systematic review was used to identify all existing factors. Secondly, frequency of factor use and accuracy of models in the reviewed studies were analysed to establish the important factors. Finally, using a questionnaire survey of CI professionals, the importance levels of factors were validated using the Cronbach's alpha reliability coefficient and significant index ranking. The findings show that the important quantitative factors are profitability, liquidity, leverage, management efficiency and cash flow. While important qualitative factors are management/owner characteristics, internal strategy, management decision making, macroeconomic firm characteristics and sustainability. These factors, which align with existing insolvency-related theories, including Porter's five competitive forces and Mintzberg's 5Ps (plan, ploy, pattern, position and perspective) of strategy, were used to develop a theoretical framework. This study contributes to the debate on the need to amalgamate qualitative and quantitative factors to develop a valid CI-IPM