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In response to escalating energy demands and global warming concerns, the Passive House Standard has emerged as a solution in residential construction, aiming to drastically reduce energy consumption and operational costs primarily through high-performance building envelopes. While a considerable volume of the literature has focused on the Passivhaus Institute (PHI) standards, predominantly in European contexts, there is a gap in research on the Passive House Institute US (Phius) standards, particularly in North American climates. This study conducts a quantitative comparative analysis of two adjacent multifamily residential buildings in Central Pennsylvania, Climate Zone 5A—one built using conventional construction methods and the other following Passive House (PHIUS+ 2015) certification standards—to validate the energy efficiency improvements attributed to Passive House designs. A comparative analysis of the whole building energy use over two years reveals that the Passive House building consumes approximately 50% less energy than its conventional counterpart in terms of whole building energy use and the national median recommended benchmark metric defined by the Energy Star Portfolio Manager. These findings emphasize the potential for significant energy savings and greenhouse gas reductions in residential buildings, highlighting the necessity for policymakers and governments to incentivize the adoption of Passive House standards to achieve environmental sustainability and reduce energy costs for society. [ABSTRACT FROM AUTHOR]

Smart Grids (SG) have smart meters and advance metering infrasturutre (AMI) which generates huge data. This data can be used for predicting energy consumption using big data analytics. A very limited work has been carried out in the literature which shows the utilization of big data in energy consumption prediction. In this paper, the proposed method is based on Genetic Algorithm - Long Short Term Memory (GA-LSTM). LSTM memorises values over an arbitrary interval that manages time series data very effictively while GA is an evolutionary process that is used for optimization. GA combines with LSTM to process hyperparameters such as hidden layers, epochs, data intervals, batchsize and activation functions. Hence, GA creates a new vector for optimum solution that provides minimum error. These methods provide the best performance when compared with existing benchmarks. Moreover, GA-LSTM is used in a multi-threaded environment which increases the speed of convergence. Here, the multi-core platform is operated for solving one-dimensional GA-based inverse scattering problems. The result shows that GA-LSTM provides better convergence as compared to random approach techniques. For validating the results, Pennsylvania-New Jersey-Maryland Interconnection (PJM) energy consumption data has been used while adopting different performance evaluation metrics. [ABSTRACT FROM AUTHOR]

The intent of this paper is to operationalize some aspects of local sustainability in a suitable development scenario and to compare its energy-use and environmental impacts to trend development. After a discussion of suburban sprawl, local sustainability, and the current state of the Pennypack Creek Watershed in the Philadelphia metro region, these residential location scenarios are presented. The latter were created using geographic information systems software and are based on projections from the Delaware Valley Regional Planning Commission. The impacts of the scenarios on energy use, air emissions, greenhouse gas emissions, water quality, and biological integrity were estimated with very few data, and the effect on the value of generic ecosystem services was assessed. The suitable development scenario was 29% better in terms of energy use and air and greenhouse gas emissions, 2.4% worse on water quality, and 2.6% better with respect to biological integrity. Given its net beneficial results, recommendations for policies to engender suitable development are made, and an outline of an implementation plan is proposed. Thoughts regarding refinements of the present work and the applicability of the methods used here to other watersheds conclude the work. [ABSTRACT FROM AUTHOR]

Energy harvesting has a vital role in building reliable Environmental Wireless Sensor Networks (EWSNs), without needing to replace a discharged battery. Solar energy is one of the main renewable energy sources that can be used to efficiently charge a battery. This paper introduces two solar energy harvesters and their power measurements at different light conditions in order to charge rechargeable AA batteries powering EWSN nodes. The first harvester is a primitive energy harvesting circuit that is built using elementary off-shelf components, while the second harvester is based on a commercial boost converter chip. To prove the effectiveness of harvesting solar energy, five EWSN nodes were distributed at a nature reserve (the Audubon Society of Western Pennsylvania, USA) and the sunlight at their locations was recorded for more than five months. For each recorded illumination, the corresponding harvested energy has been estimated and compared with the average energy consumption of the EWSN with the most power consumption. The results show that the daily harvested energy effectively compensates for the energy consumption of the EWSN nodes, and the battery charge capacity of 295 mAh can reliably support their daily dynamic energy consumption. [ABSTRACT FROM AUTHOR]

In recent years, a large number of building energy consumption prediction models, with various intended uses, have been proposed. The majority of these models have either taken a data-driven or a physical modeling approach, with each approach having its own strengths and limitations. Towards leveraging the strengths and reducing the limitations of each approach for improved prediction performance, this paper presents a hybrid machine-learning approach for occupant-behavior-sensitive energy consumption prediction. The proposed approach is composed of three constituent models: (1) a machine-learning model that learns the impact of outdoor weather conditions from simulation-generated data, (2) a machine-learning model that learns the impact of occupant behavior from real data, and (3) an ensemble model that predicts cooling energy consumption based on the outputs of the first two models. The simulation-generated data were created through simulating a set of reference buildings in EnergyPlus. The real data were collected from an office building in Pennsylvania. The proposed hybrid model was validated on an unseen real dataset. It achieved 0.73 kWh RMSE and 9.07% CV in hourly cooling energy consumption prediction, which indicates that the proposed approach is promising. [ABSTRACT FROM AUTHOR]

Fourth industrial revolution is the introduction of ICT (mostly IoT) in industry and elsewhere, which enables the creation of cyber-physical systems, i.e., digital twins of reality. The application of widespread digitization of processes brings changes in terms of increased efficiency of processes, increased flexibility of production, and the possibility of realizing prosocial and pro-ecological goals, such as sustainable development, sustainable production and consumption, and reducing the consumption of increasingly expensive energy. Nowadays, the high autonomy of cyber-physical systems and benefits to society are expected by including human factors within the Industry 5.0 concept. Implementing the Fourth Industrial Revolution technologies and meeting the expectations of sustainable development also means new challenges for the knowledge and skills of industry employees, mainly engineers implementing modern solutions. Hence, the article's aim is to identify the critical knowledge and skills of engineers responsible for implementing the Fourth Industrial Revolution technologies. The achievements and results presented in the article were obtained based on research conducted among experts from the University of Pennsylvania (USA). The study considers aspects connected to the problems of implementing the Fourth Industrial Revolution technologies and identifies the benefits and risks of their implementation for Society 5.0. [ABSTRACT FROM AUTHOR]

Since the Pennsylvania oil rush of 1859, petroleum has quickly become the dominant fuel of industrial society. The "Peak Oil" debate focused on whether or not there was an impending production crunch of cheap oil, and whilst there have been no shortages across the globe, a shift from conventional to unconventional oil liquids has occurred. One aspect of this shift was not fully explored in previous discussions–although of some importance in a low-carbon energy transition context: the extent to which the net-energy supply of oil products is affected by the use of lower quality energy sources. To fill this gap, this paper incorporates standard EROI (energy-return-on-investment) estimates and dynamic decline functions in the GlobalShift all-liquids bottom-up model on a global scale. We determine the energy necessary for the production of oil liquids (including direct and indirect energy costs) to represent today 15.5% of the energy production of oil liquids, and growing at an exponential rate: by 2050, a proportion equivalent to half of the gross energy output will be engulfed in its own production. Our findings thus question the feasibility of a global and fast low-carbon energy transition. We therefore suggest an urgent return of the peak oil debate, but including net-energy issues and avoiding a narrow focus on 'peak supply' vs 'peak demand'. [Display omitted] • Global gross and net-energy of oil liquids production is determined from 1950 to 2050. • Energy required for production is estimated to be 15.5% of the actual gross energy. • Oil liquids become a limit to a rapid and global low-carbon energy transition. • The peak supply vs. peak demand dispute needs to be re-examined. • Focus should be put instead on net-energy transition and wise energy consumption. [ABSTRACT FROM AUTHOR]

• Background is provided about the need for tools to support regional energy baselining and efficiency planning. • 296 homes in Pennsylvania are simulated in EnergyPlus using a batch simulation tool and energy audit records. • Simulation results are scaled and compared to building level electricity use data and utility sales data. • Analysis reveals discrepancies between simulated and actual energy use that varies by utility. • The role of building simulation tools for energy baselining and efficiency planning is discussed. Building simulation tools are seeing growing use in the residential sector due to a combination of increased interest in building energy performance, more user-friendly software packages, and decreasing computing power requirements. These tools are now being applied in batch simulations, where large numbers of homes are simulated at once to understand existing building stock energy consumption and potential energy efficiency improvements to make to those stocks. As these types of applications grow, attention is needed to ensure that simulation inputs and outputs are handled with an appropriate understanding of the uncertainties involved. In this paper, we use a batch simulation tool to translate energy audit records of 296 existing single-family homes in Pennsylvania into EnergyPlus building simulation models. We compare the results of these simulations to actual metered electricity use data from these homes, representative device-level data, and local utility data to better understand the abilities and limits of such modeling practices. Results show that while batch simulations are capable of producing accurate estimates of the energy consumption of a large number of homes, many more homes had their energy consumption overestimated than underestimated, resulting in a significant net overestimate for the sample as a whole. When results are scaled up to the utility level, simulation accuracy varies by utility with net overestimates ranging from around 15% to 25%. Comparisons of individual simulation results to metered consumption identify electric space heating systems as the source of much of this error. This highlights the importance of gathering accurate, detailed information on these systems to generate reliable simulation results. NREL has recently released a batch simulation tool called ResStock that takes a more statistically robust approach to simulating representative archetype homes as opposed to actual existing homes. The results of this study suggest that the type of probabilistic treatment of building characteristics underlying that tool will provide more meaningful results than trying to simulate actual buildings. With additional validation measures similar to those used in this study, ResStock can likely be used to reliably estimate baseline energy consumption and quantify efficiency savings opportunities and can serve as a valuable decision-making tool for state, local, and utility efficiency program designers. [ABSTRACT FROM AUTHOR]