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In developing countries, such as Brazil, the construction sector is consistently focused on the construction of new buildings, and there is no dissemination of the preservation, restoration and maintenance of historic buildings. Idle buildings, due to the use and lack of maintenance, present pathological manifestations, such as moisture problems that compromise specially their thermal and energy performance. With this in mind, the purpose of this work is to create a digital model using terrestrial photogrammetry and suggest retrofit interventions based on computer simulation to improve the thermal and energy performance of a historical building.

The proposed methodology combined terrestrial photogrammetry using common smartphones and commercial software for historical buildings with building information modeling (historic building information modeling (HBIM)) and building energy modeling (BEM). The approach follows five steps: planning, site visit, data processing, data modeling and results. Also, as a case study, the School of Architecture and Urbanism of the Fluminense Federal University, built in 1888, was chosen to validate the approach.

A digital map of pathological manifestations in the HBIM model was developed, and interventions considering the application of expanded polystyrene in the envelope to reduce energy consumption were outlined. From the synergy between HBIM and BEM, it was concluded that the information modeled using photogrammetry was fundamental to create the energy model, and simulations were needed to optimize the possible solutions in terms of energy consumption.

Firstly, the work proposes a reasonable methodology to be applied in development countries without sophisticated technologies, but with acceptable precision for the study purpose. Secondly, the presented study shows that the use of HBIM for energy modeling proved to be useful to simulate possible solutions that optimize the thermal and energy performance.

The purpose of the work is to elaborate a model framework that includes location related temporal characteristics in energy supply and demand. These characteristics in mind an imaginable energy system setup can be explored with the framework. In a case study the possible coverage of the global energy demand, by solar‐ and wind power in junction with a backup technology is treated.

Spatially and temporally high disaggregated data describing different aspects of the energy supply side (especially devoted to renewable resources and related availabilities) as well as the energy demand side are investigated. This information is processed to serve as input for the TIMES model generator in a special adapted model. The complete workflow is enclosed in a graphical user interface implemented as a plugin in the software package ArGIS.

The elaborated case study shows the practicability of the approach to treat spatially and temporally high disaggregated problems in the energy system. Especially sensibilities of an optimal system setup in dependency on assumptions on specific costs for energy transport or storage can be investigated in a very detailed manner.

Since the spatial and temporal disaggregated examination implies the treatment of huge datasets, simplifications have to be made in the description of the technological setup of the energy system. The approach is appropriate to describe single scenario set‐ups but not a complete forecast based system development.

Geographic information systems (GIS) and geographic information are tied together with a conventional modeling approach of energy systems. That enables the cognition and quantification of influences and sensibilities related to spatial and temporal deviations in our energy system either on the supply or the demand side.

Maintenance is a critical business function with a great impact on economic, environmental and social aspects. However, maintenance decisions' planning has been driven by merely economic and technical measures with inadequate consideration of environmental and social dimensions. This paper presents a review of the literature pertaining to sustainable maintenance decision-making models supported by a bibliometric analysis that seeks to establish the evolution of this research over time and identify the main research clusters.

A systematic literature review, supported with a bibliometric and network analysis, of the extant studies is conducted. The relevant literature is categorized based on which sustainability pillar, or possibly multiple ones, is being considered with further classification outlining the application area, modeling approach and the specific peculiarities characterizing each area.

The review revealed that maintenance and sustainability modeling is an emerging area of research that has intensified in the last few years. This fertile area can be developed further in several directions. In particular, there is room for devising models that are implementable, based on reliable and timely data with proven tangible practical results. While the environmental aspect has been considered, there is a clear scarcity of works addressing the social dimension. One of the identified barriers to developing applicable models is the lack of the required, accurate and timely data.

This work contributes to the maintenance and sustainability modeling research area, provides insights not previously addressed and highlights several avenues for future research. To the best of the authors' knowledge, this is the first review that looks at the integration of sustainability issues in maintenance modeling and optimization.

Material extrusion (MEX) is a class of additive manufacturing (AM) process based on MEX principle. In the viewpoint of Industry 4.0 and sustainable manufacturing, AM technologies are gaining importance than conventional manufacturing route (subtractive manufacturing). Because of the ease of use and lesser operation skills, MEX had wide popularity in industry for product and prototype development. This study aims to analyze energy consumption of MEX-based AM process and its influencing factors.

A group of factors were identified pertaining to MEX-based AM process. In this viewpoint, this study presents the configuration of a structural model using interpretive structural modeling (ISM) to depict dominant factors in MEX-based AM process. A total of 18 influencing factors are identified and ranked using ISM methodology for MEX process. The Impact Matrix Cross-reference Multiplication Applied to a Classification analysis was done to categorize influencing factors into four groups for MEX-based AM process.

The derivation of structural model would enable AM practitioners to systematically analyze the factors and to derive key factors which enable comprehensive energy modeling and energy assessment studies. Also, it facilitates the development of energy efficient AM system.

The development of structural model for analysis of factors influencing energy consumption of MEX-based AM is the original contribution of the authors.

The purpose of this paper is to present a methodology to quantify the solar energy potential for applying photovoltaic systems and find an efficient geometry for urban blocks to obtain a better quality of daylighting in terms of continuous daylight autonomy (DA) and spatial DA with less energy consumption.

The paper is based on a complete simulation of the topography and micro-climate of the area under study. Simulations were performed using ArcGIS and Rhinoceros and urban daylight (UD) and urban modeling interface plugin for a neighborhood in the region of Narmak in Tehran, Iran. Five configurations of a neighborhood were compared using simulations.

It was found that the impact of the geometrical form on daylight gain and energy consumption is significant and the terraced model is the most suitable form for obtaining a constant floor area ratio. Furthermore, it is an optimal form of urban blocks to gain the most energy through photovoltaic systems in the neighborhood as it would be able to satisfy about 42 percent of the energy needs.

Planning to achieve sufficient energy factors in cities is a difficult task, since urban planners often do not have adequate technical knowledge to measure the contribution of solar energy in urban plans and this paper aims to introduce a comprehensive modeling methodology by which the urban energy planning can be used and understood in the urban context to make it completely clear as a strategy of implementation.

The purpose of this paper is to assess model risk with regard to wind energy output in monthly cash flow models for the purpose of valuation and risk assessment of wind farm investments, where only a few approaches exist in the literature.

This paper focuses on the risk-return characteristics of this investment from the perspective of private and institutional investors and takes into account several risks, in particular the resource risk related to the uncertainty of the monthly wind energy produced. To this end, this paper presents different approaches for modeling monthly wind power output and assesses the impact of three selected models with different properties on the investment’s risk-return characteristics by means of a stochastic discounted cash flow model. In addition, the model considers the possibility of a joint operation of the wind farm with a pumped hydro storage system to reduce risk and improve profits.

The results show that the (non-)consideration of seasonality of the monthly wind energy produced considerably influences the risk-return characteristics, but that principal developments dependent on input parameters and model variables remain similar.

This paper contributes to the literature by presenting different approaches for modeling the monthly wind energy produced based on direct models of the wind energy output, which are rare in the existing literature. Further, their impact on risk-return characteristics of a wind farm investment is analyzed, and thus, related model risk is assessed.

This paper aims to use artificial neural networks to develop models for forecasting energy demand for Australia, China, France, India and the USA.

The study used quarterly data that span over the period of 1980Q1-2015Q4 to develop and validate the models. Eight input parameters were used for modeling the demand for energy. Hyperparameter optimization was performed to determine the ideal parameters for configuring each country’s model. To ensure stable forecasts, a repeated evaluation approach was used. After several iterations, the optimal models for each country were selected based on predefined criteria. A multi-layer perceptron with a back-propagation algorithm was used for building each model.

The results suggest that the validated models have developed high generalizing capabilities with insignificant forecasting deviations. The model for Australia, China, France, India and the USA attained high coefficients of determination of 0.981, 0.9837, 0.9425, 0.9137 and 0.9756, respectively. The results from the partial rank correlation coefficient further reveal that economic growth has the highest sensitivity weight on energy demand in Australia, France and the USA while industrialization has the highest sensitivity weight on energy demand in China. Trade openness has the highest sensitivity weight on energy demand in India.

This study incorporates other variables such as financial development, foreign direct investment, trade openness, industrialization and urbanization, which are found to have an important effect on energy demand in the model to prevent underestimation of the actual energy demand. Sensitivity analysis is conducted to determine the most influential variables. The study further deploys the models for hands-on predictions of energy demand.

Obviously, the development of a robust optimization framework is the main step in energy and climate policy. In other words, the challenge of energy policy assessment requires the application of approaches which recognize the complexity of energy systems in relation to technological, social, economic and environmental aspects. This paper aims to develop a two-sided multi-agent based modelling framework which endogenizes the technological learning mechanism to determine the optimal generation plan. In this framework, the supplier agents try to maximize their income while complying with operational, technical and market penetration rates constraints. A case study is used to illustrate the application of the proposed planning approach. The results showed that considering the endogenous technology cost reduction moves optimal investment timings to earlier planning years and influences the competitiveness of technologies. The proposed integrated approach provides not only an economical generation expansion plan but also a cleaner one compared to the traditional approach.

To the best of the authors’ knowledge, so far there has not been any agent-based generation expansion planning (GEP) incorporating technology learning mechanism into the modelling framework. The main contribution of this paper is to introduce a multi-agent based modelling for long-term GEP and undertakes to show how incorporating technological learning issues in supply agents behaviour modelling influence on renewable technology share in the optimal mix of technologies. A case study of the electric power system of Iran is used to illustrate the usefulness of the proposed planning approach and also to demonstrate its efficiency.

As seen, the share of the renewable technology agents (geothermal, hydropower, wind, solar, biomass and photovoltaic) in expanding generation increases from 10.2% in the traditional model to 13.5% in the proposed model over the planning horizon. Also, to incorporate technological learning in the supply agent behaviour leads to earlier involving of renewable technologies in the optimal plan. This increased share of the renewable technology agents is reasonable due to their decreasing investment cost and capability of cooperation in network reserve supply which leads to a high utilization factor.

To the best of the authors’ knowledge, so far there hasn’t been any agent-based GEP paying attention to this integrated approach. The main contribution of this paper is to introduce a multi-agent based modelling for long-term GEP and undertakes to show how incorporating technological learning issues in supply agents behaviour modelling influence on renewable technology share in the optimal mix of technologies. A case study of the electric power system of Iran is used to illustrate the usefulness of the proposed planning approach and also to demonstrate its efficiency.

Through the study of visualizations, virtual worlds and information exchange, the purpose of this paper is to reveal the complex connections between technology and the work of design and construction. The authors apply the sociotechnical view of technology and the ramifications this view has on successful use of technology in design and construction.

This is a discussion paper reviewing over a decade of research that connects three streams of research on architecture, engineering and construction (AEC) teams as these teams grappled with adapting work practices to new technologies and the opportunities these technologies promised.

From studies of design and construction practices with building information modeling and energy modeling, the authors show that given the constructed nature of models and the loose coupling of project teams, these team organizational practices need to mirror the modeling requirements. Second, looking at distributed teams, whose interaction is mediated by technology, the authors argue that virtual world visualizations enhance discovery, while distributed AEC teams also need more traditional forms of 2D abstraction, sketching and gestures to support integrated design dialogue. Finally, in information exchange research, the authors found that models and data have their own logic and structure and, as such, require creativity and ingenuity to exchange data across systems. Taken together, these streams of research suggest that process innovation is brought about by people developing new practices.

In this paper, the authors argue that technology alone does not change practice. People who modify practices with and through technology create process innovation.

This paper reviews the literature on maintenance strategies for energy efficiency as a potential maintenance approach. The purpose of this paper is to identify the main concept and common principle for each maintenance strategy for energy efficiency.

A literature review has been carried out on maintenance and energy efficiency. The paper systematically classified the literature into three maintenance strategies (e.g. inspection-based maintenance [IBM], time-based maintenance [TBM] and condition-based maintenance [CBM]). The concept and principle of each maintenance strategy are identified, compared and discussed.

Each maintenance strategy's main concept and principle are identified based on the following criteria: data required and collection, data analysis/modeling and decision-making. IBM relies on human senses and common senses to detect energy faults. Any detected energy losses are quantified to energy cost. A payback period analysis is commonly used to justify corrective actions. On the other hand, CBM monitors relevant parameters that indicate energy performance indicators (EnPIs). Data analysis or deterioration modeling is needed to identify energy degradation. For the diagnostics approach, the energy degradation is compared with the threshold to justify corrective maintenance. The prognostics approach estimates when energy degradation reaches its threshold; therefore, proper maintenance tasks can be planned. On the other hand, TBM uses historical data from energy monitoring. Data analysis or deterioration modeling is required to identify degradation. Further analysis is performed to find the optimal time to perform a maintenance task. The comparison between housekeeping, IBM and CBM is also discussed and presented.

The literature on the classification of maintenance strategies for energy efficiency has been limited. On the other hand, the ISO 50001 energy management systems standard shows the importance of maintenance for energy efficiency (MFEE). Therefore, to bridge the gap between research and industry, the proposed concept and principle of maintenance strategies will be helpful for practitioners to apply maintenance strategies as energy conservation measures in implementing ISO 50001 standard.

The novelty of this paper is in-depth discussion on the concept and principle of each maintenance strategy (e.g. housekeeping or IBM, TBM and CBM) for energy efficiency. The relevant literature for each maintenance strategy was also summarized. In addition, basic rules for maintenance strategy selection are also proposed.