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This study investigates the impact and economic viability of energy-efficient building envelope and orientation for contributing net zero energy building (NZEB) and suggests optimum thermal insulation thickness, optimum wall thickness, appropriate orientation and glazing types of window in the contexts of unique Bangladeshi subtropical monsoon climate.

The whole study was conducted through energy simulation perspective of an existing office building using building information modeling (BIM) and building energy modeling (BEM) tools which are Autodesk Revit 2017, Autodesk Green Building Studio (GBS) and eQUEST. Numerous simulation patterns were created for energy simulation considering building envelope parameters and orientations. A comprehensive data analysis of simulation results was conducted to sort out efficient passive design strategies.

The optimum thermal mass and thermal insulation thickness are 6.5 and 0.5 inches, respectively, considering energy performance and economic viability. This study highly recommends that a building should be designed with a small window-to-wall ratio in the south and west face. The window should be constructed with double glazing Low-E materials to reduce solar heat gain. The studied building saves 9.14% annual energy consumption by incorporating the suggested passive design strategies of this study.

The output of this work can add some new energy-efficient design strategies to Bangladesh National Building Code (BNBC) because BNBC has not suggested any codes or regulations regarding energy-efficient passive design strategies. It will also be useful to designers of Bangladesh and other countries with similar subtropical climatic contexts which are located in Southeast Asia and Northern Hemisphere of Earth.

This paper aims to develop an architecture for 3D printers in an Industrial Internet of Things (IIoT) controlled automated manufacturing environment. An algorithm is proposed to estimate the electrical energy consumption of 3D printing jobs, which is used, 3D Printing, Sustainable Manufacturing, Industry 4.0, Electrical Energy Estimation, IIoT to schedule printing jobs on optimal electrical tariff rates.

An IIoT-enabled architecture with connected pools of 3D printers and an Electrical Energy Estimation System (EEES) are used to estimate the electrical energy requirement of 3D printing jobs. EEES applied the combination of Maximum Likelihood Estimation and a dynamic programming–based algorithm for estimating the electrical energy consumption of 3D printing jobs.

The proposed algorithm decently estimates the electrical energy required for 3D printing and able to obtain optimal accuracy measures. Experiment results show that the electrical energy usage pattern can be reconstructed with the EEES. It is observed that EEES architecture reduces the peak power demand by scheduling the manufacturing process on low electrical tariff rates.

Proposed algorithm is validated with limited number of experiments.

IIoT with 3D printers in large numbers is the future technology for the automated manufacturing process where controlling, monitoring and analyzing such mass numbers becomes a challenging task. This paper fulfills the need of an architecture for industries to effectively use 3D printers as the main manufacturing tool with the help of IoT. The electrical estimation algorithm helps to schedule manufacturing processes with right electrical tariff.

Most energy planning exercises are carried out with aggregate data at the national level. At regional level namely village, block/district, there have been fewer efforts for energy planning. This paper aims to present a conceptual framework for analyzing energy consumption pattern at rural domestic sector. The entire framework is designed in such a way that user is provided with helpful tips and context-sensitive help options.

Decision support system (DSS) is developed with a graphical user interface (GUI) which helps to compute domestic energy consumption at a specific location. This user interface is fully menu-driven GUI in which different types of data are handled, maintained and displayed. Using this GUI, administrator can generate various reports regarding energy requirements from which decision maker can analyse the energy consumption pattern, per capita energy consumption (PCEC), adult equivalent, etc.

DSS assists in analyzing the energy sources and demand spatially. The technologies and methods used to develop and deploy DSS to aid in domestic energy consumption make work easier for a decision maker. GUI provides user an easy access of data analysis and the design and evaluation of domestic energy consumption strategies. DSS is validated with the data pertaining to energy situation of a block in central India. Stratified sampling survey, energy analysis covering 100 households from ten villages revealed that the average value of PCEC (in kWh/day) resource-wise ranges and activity wise for the surveyed block are as follows: fuel wood (0.60), dung cake (0.085), kerosene (0.18), liquefied petroleum gas (0.052) and electricity energy for lighting and appliances (0.353) and on the other hand it is observed that cooking PCEC is highest (0.505), followed by heating (0.24), lighting (0.162), cooling (0.162) and electrical appliances (0.108).

Energy analysis shows energy DSS will improve the quality of decision making at the block, district, and state level and enable the analysis and understanding of energy impacts of various decisions. Considering the Indian rural energy availability scenario, possible renewable energy solutions are also suggested to meet the current domestic energy requirements partially or fully.

The two main contributing factors that control the overall buildings’ energy performance are the heating ventilation and air conditioning (HVAC) system and the envelope design. Environmental design guidelines that consider these two factors aim to lower energy consumption. However, they are regional and climate-sensitive. This study aims to investigate how three main buildings’ envelope design variables (orientation, compactness and window to wall ratio) impact the overall building’s energy consumption within Kuwait’s regional and climate conditions.

This study simulate the energy consumption of typically shaped buildings by varying their geometry between a square to a rectangular floor plan. This study analyse the associated energy usage and provide early-stage envelope design guidance specific to the country’s conditions, to make informed decisions towards environmentally conscious buildings.

The analysed envelope variables have the potential to reduce energy consumption by 40%, and the possibility to reduce HVAC system capacity by 30%. In contrast to the general guidance in literature and standards, the simulation results demonstrate that less compact building forms perform on occasions better than the most compact ones.

The objective of this paper is to quantify the energy consumption rates for buildings located within the Arabian Peninsula, an under-studied region with potentially high interest considering three main envelope design variables. The buildings’ yearly energy consumption patterns are unique and suggest different envelope design considerations, compared to other regions with different climate conditions. This emphasises the importance of regional guidelines for the different factors associated with energy and buildings’ environmental performance.

In working towards a sustainable campus of public universities, energy consumption behaviour assessment is one of the several issues that requires attention by the facilities manager. Information on energy consumption behaviour is needed to determine potential energy savings. The purpose of this study is to assess energy consumption behaviour for student accommodations in Malaysian public universities.

This study focuses on developing energy consumption behaviour models (ECBMs) and assesses the potential energy savings. The “energy culture” framework consolidated with multiple regression analysis is used to strengthen the development of ECBMs. A self-administrated survey involving 1,009 respondents in selected public universities was carried out.

The result shows that five factors from the energy culture framework contribute to energy consumption behaviour, namely, building regulation, environmental concern, education, social marketing and direct factors (device and activities). These factors are included in the model for predicting energy consumption levels. The results show that there is a 78 per cent difference in energy consumption between the observed and predicted data.

This study indicates a high potential energy saving among students of Malaysian public universities.

The model was tested against the overall students among Malaysian public universities. In future, the model can be tested within hostel accommodations. The present assessment revealed the potential energy saving among the hostel buildings and sets the target regarding which building has a potential to reduce energy. It also helps the facilities managers to come up with strategies for programmes and energy policy in public universities.

The study attempts to capture the comprehensive accounting framework of the inverted U-shaped Environmental Kuznets Curve (EKC) hypothesis relevant for an emerging economy based on an emission-growth decoupling approach. The paper intends to re-examine and analyze the roles of influential production- and consumption-based drivers for the prominently observable increasing pattern of the energy-related carbon dioxide (CO₂) emissions from the Indian Territory.

The study adopted an annual time series structural decomposition analysis (SDA) based on a single-country input-output framework for the period 2000–2014 to identify and elaborate the contribution of the responsible drivers to the production-based carbon emission of India. The study further proceeded to analyze a decoupling index to explore the features of economic growth and carbon emissions comovement over time.

The results from the empirical exercise reflect a pattern of consistently developing relative decoupling character for most of the production-based drivers. The paper produces insightful results on the pattern of energy-related CO₂ emissions from the perspective of the EKC hypothesis and highlights the importance of consumption-based drivers as substantial contributors to the economy-wide CO₂ emissions to be controlled for effective decoupling of the aggregate production-based CO₂ from the volume of aggregate production to enhance the opportunities for sustainable economic development.

The study uniquely correlates the declining trend of the emission intensity of India's gross domestic product (GDP) and the inclining trend of the overall emissions due to ever-increasing gross output in the form of a comprehensive accounting relationship.

Power management in households has become the periodic issue for electric suppliers and household occupants. The number of electronic appliances is increasing day by day in every home with upcoming technology. So, it is becoming difficult for the energy suppliers to predict the power consumption for households at the appliance level. Power consumption in households depends on various factors such as building types, demographics, weather conditions and behavioral aspect. An uncertainty related to the usage of appliances in homes makes the prediction of power difficult. Hence, there is a need to study the usage patterns of the households appliances for predicting the power effectively.

Principal component analysis was performed for dimensionality reduction and for finding the hidden patterns to provide data in clusters. Then, these clusters were further being integrated with climate variables such as temperature, visibility and humidity. Finally, power has been predicted according to climate using regression-based machine learning models.

Power prediction was done based on different climatic conditions for electronic appliances in the residential sector. Different machine learning algorithms were implemented, and the result was compared with the existing work.

This will benefit the society as a whole as it will help to reduce the power consumption and the electricity bills of the house. It will also be helpful in the reduction of the greenhouse gas emission.

The proposed work has been compared with the existing work to validate the current work. The work will be useful to energy suppliers as it will help them to predict the next day power supply to the households. It will be useful for the occupants of the households to complete their daily activities without any hindrance.

The Organization for Economic Cooperation and Development (OECD) countries are facing unprecedented challenges related to climate change and population aging. The purpose of the analysis is to explore the relationship between population aging and environmental degradation, accounting for human capital, using a sample of 19 OECD countries over the period 1980–2019.

On the empirical methodology, the analysis uses panel estimators with heterogenous coefficients and an error structure that takes into consideration cross-country heterogeneity and cross-sectional dependence for a panel of 19 OECD countries over the period 1980–2019. To examine the relationship between population aging and environmental degradation, the authors employ two alternative measures of environmental degradation that is energy consumption and CO2 emissions in metric tons per capita. Concerning the regressors, the authors account for two alternative aging indicators, namely the elderly population and the old-age dependency ratios to confirm robustness.

The analysis provides evidence that population aging and human capital development (IHC) lead to lower energy consumption in the OECD sample. Overall, the growing number of elderly people in the OECD seems to act as a mitigating factor for energy consumption. The authors view these results as conveying the message that the evolution of population aging along with channeling government expenditures towards human capital enhancement are important drivers of curbing energy consumption and ensuring environmental sustainability. The authors' research is of great significance for environmental policymakers by illuminating the favorable energy consumption patterns that population aging brings to advanced economies.

The main limitation of this study concerns data availability. Future research, and subject to greater data availability in the future, could dig deeper into understanding the dynamics of this complex nexus by incorporating additional control variables. Similarly, the authors focus on aggregate renewable energy consumption, and the authors do not explicitly model the sources of renewable energy (wind, hydropower, solar power, solid biofuels and other). Additional analysis of the breakdown of renewable energy sources would be insightful – subject to data availability – especially for meeting the recently agreed new target of 42.5% for European Union (EU) countries by 2030. A deep transformation of the European energy system is needed for the EU to meet the target. Finally, extending the model to include a range of non-OECD countries that are also experiencing demographic transformations is a promising avenue for future research.

To the best of the authors' knowledge, this study is the first to examine the effects of population aging and human capital on environmental degradation using a broad set of OECD countries and advanced spectrum estimation methods. Given cross-sectional dependencies and cross-country heterogeneity, the authors' empirical results underline the importance of cross-OECD policy spillovers and knowledge diffusions across the OECD countries. The new “energy culture” calls for concerted policy action even in an aging era.

On the one hand, this paper is to further understand the residents' differentiated power consumption behaviors and tap the residential family characteristics labels from the perspective of electricity stability. On the other hand, this paper is to address the problem of lack of causal relationship in the existing research on the association analysis of residential electricity consumption behavior and basic information data.

First, the density-based spatial clustering of applications with noise method is used to extract the typical daily load curve of residents. Second, the degree of electricity consumption stability is described from three perspectives: daily minimum load rate, daily load rate and daily load fluctuation rate, and is evaluated comprehensively using the entropy weight method. Finally, residential customer labels are constructed from sociological characteristics, residential characteristics and energy use attitudes, and the enhanced FP-growth algorithm is employed to investigate any potential links between each factor and the stability of electricity consumption.

Compared with the original FP-growth algorithm, the improved algorithm can realize the excavation of rules containing specific attribute labels, which improves the excavation efficiency. In terms of factors influencing electricity stability, characteristics such as a large number of family members, being well employed, having children in the household and newer dwelling labels may all lead to poorer electricity stability, but residents' attitudes toward energy use and dwelling type are not significantly associated with electricity stability.

This paper aims to uncover household socioeconomic traits that influence the stability of home electricity use and to shed light on the intricate connections between them. Firstly, in this article, from the perspective of electricity stability, the characteristics of the power consumption of residents' users are refined. And the authors use the entropy weight method to comprehensively evaluate the stability of electricity usage. Secondly, the labels of residential users' household characteristics are screened and organized. Finally, the improved FP-growth algorithm is used to mine the residential household characteristic labels that are strongly associated with electricity consumption stability.

1. The stability of electricity consumption is important to the stable operation of the grid.

2. An improved FP-growth algorithm is employed to explore the influencing factors.

3. The improved algorithm enables the mining of rules containing specific attribute labels.

4. Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

The stability of electricity consumption is important to the stable operation of the grid.

An improved FP-growth algorithm is employed to explore the influencing factors.

The improved algorithm enables the mining of rules containing specific attribute labels.

Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

This paper seeks to describe a protocol to estimate annual community energy consumption baselines for single‐family detached homes in the Gainesville Regional Utility service area of Alachua County, Florida, USA. Further, it details methods using these baselines to make direct comparisons of individual households' energy consumption and evaluate the energy impacts of three prescriptive demand side management (DSM) programs.

To improve estimates of energy savings, the paper proposes using a “micro” scale multivariate regression methodology based on a census of utility and property appraiser household data.

Results suggest that traditional analysis approaches are likely to overestimate savings significantly and that the annual community baseline technique provides more consistent estimates of energy savings than most commonly used methods.

This type of analysis could provide a tool that utilities can use to more accurately and cost effectively measure DSM savings. This could result in reduced energy demand related to streamlined program setup and management.

The proposed methodology is unique in that it defines a new household‐level energy consumption baseline measure that we think is a more appropriate performance measure, uses a census of publicly available data for the population of interest, merging metered utility data with property appraiser data, and works upward to construct a simple model for evaluating household‐level energy consumption. The critical element that distinguishes our proposed energy performance measures is that they are calculated and interpreted using annual, population‐level, comparison‐group baselines that effectively normalize for community energy consumption patterns in any given year.