Energy Efficiency最新文献

In many countries, residential energy consumption constitutes a significant portion of total energy usage, making it a crucial focus for power systems and urban planners. Addressing energy consumption in buildings involves two primary facets: accurately predicting and optimizing load demand. This research aims to address the challenges of load demand prediction in the context of building energy consumption. It introduces innovative approaches, including optimized support vector regression (SVR), temperature factor consideration, and the integration of Trombe walls (TW), ultimately contributing to more accurate load demand forecasts and enhanced energy efficiency. To enhance the precision of load demand prediction, we introduce a novel variable that quantifies deviation from the ideal temperature; a key factor in energy usage. This innovative temperature factor plays a pivotal role in forecasting the load demand more accurately. Leveraging this novel approach, we employ an optimized (SVR) that considers weather conditions. The parameters of a radial basis function kernel-based support vector regression (RBF-SVR) method are fine-tuned through an improved particle swarm optimization algorithm (IPSO). In addition, installed TW prove highly effective in reducing building energy consumption by harnessing and redistributing heat, consequently improving the load demand profile. We employ mathematical models to analyze the impact of Trombe walls on predicted load demands, demonstrating that our proposed method yields low mean absolute percentage error (MAPE) when applied to sample buildings. The findings reveal that our method significantly enhances the accuracy of energy usage prediction, while the installation of Trombe walls results in a remarkable reduction of load demand – by 19.32% in winter and 16.24% in summer, thereby promoting energy efficiency and sustainability.

We study the impact of energy price changes on energy consumption intensity and its substitution effect on consumption intensity of labor, capital and raw materials, through the decomposition of input consumption intensity in Iranian manufacturing industries from 2006 to 2018. Using translog cost function, we find that increase in energy prices has a negative effect on energy intensity of all manufacturing industries and thus increasing energy prices as a strategy to reduce energy intensity is effective, although its effect on different manufacturing industries are not the same. Moreover, we find that the effect of energy price changes on consumption intensity of capital and raw material is small, while its effect on consumption intensity of energy and labor is large. An overall assessment of the change in energy prices on the input consumption intensity shows that an increase in energy prices improves not only energy efficiency but also the efficiency of labor and capital consumption. However, relative performance of energy price reform in Iran depends on general economic conditions, as well as the process of price changes.

In non-residential buildings, building energy management systems (BEMS) and the application of data hold significant promise in reducing energy consumption. Nevertheless, BEMS have different levels of complexity, benefit, and limitation. Despite the advanced technologies and improvements in building operation, there is a clear gap in the actual performance of buildings that has been attributed to the adoption of advanced technologies. Consequently, there is an increasing need for researchers and practitioners to study current practices in order to identify and address the challenges that compromise the core objectives of BEMS. For this reason, this paper aims to validate three research questions: (i) to examine the current state of BEMS and its functionalities; (ii) to analyze the type of control used; (iii) and to determine the availability of historical data compiled by BEMS and its application in non-residential buildings. A survey of 676 buildings and interviews with building professionals were conducted. The findings confirmed that most of the buildings applied BEMS with scheduled control. In addition, a lack of digitized data for analysis and predictions was detected. Indeed, only 0.60% of the investigated buildings implemented predictive control. Finally, using hierarchical clustering analysis, responses were grouped to analyze similarities between them. The study findings help to develop targeted actions for implementing predictive control in non-residential buildings.

To overcome financing barriers and to ensure that the progress towards energy transition will continue, alternative financing concepts are required. A promising alternative may be financial citizen participation, where private individuals will contribute to the deployment of renewable energy sources (RES) by investing via various business models and financing concepts. This paper aims to promote energy transition by informing the design of policies and strategies for mobilizing citizen investment. Specific objectives are to investigate citizens’ willingness-to-invest (WTI) and to compare the factors influencing WTI in five renewable types, wind, solar, geothermal, hydroelectric energy and biomass. To meet these objectives, our study collected a representative sample of 1,536 citizens in Greece, an EU member state that stands at a critical point in energy transition due to financing barriers and social opposition to renewable projects. Our results showed that most citizens were willing to invest, but would invest mainly low sums pointing at the need to establish investment mechanisms suitable for citizens. Moreover, the factors influencing willingness-to-invest differ significantly for each renewable type highlighting the need to leverage this differentiation in marketing strategies. While perceived barriers and economic motives were important for almost all renewable types, environmental attitudes were less influential suggesting that strategies using the argument of environmental benefits may be ineffective. Findings from this study provide a precise idea on the factors affecting WTI in five renewable types and can offer significantly higher precision for the design of policies and strategies aiming at mobilizing citizen investment in renewables.

Elevator systems serve as the primary mode of transportation in tall buildings which consumes approximately 5–15% of a building's total energy demand. This research explores the potential for energy savings in elevator systems while maintaining passenger comfort through the implementation of green approaches. The research concentrates on a contemporary high-rise office building situated in the central business district of a developing economy. It employs a case study methodology involving traffic simulation to determine optimal elevator specifications for industry-standard service levels. By conducting the simulation, the researchers identified the optimal number of elevators, elevator capacity, speed, and the most suitable elevator management system. Following that, a range of green measures were implemented, including the incorporation of a regenerative system, to effectively reduce the electrical energy consumption of the elevator system. Subsequently, a passenger traffic simulation model was integrated with an energy calculation model to jointly simulate and calculate the elevator system's overall energy consumption and regeneration. The elevator energy requirements were optimized, while maintaining user-friendliness and requirements related to guidelines given in the standards. The results showed that 36% of the energy consumption was reduced by incorporating an energy regenerative option into the elevator system in addition to selecting efficient mechanical components and implementing an efficient elevator traffic management system. This research contributes to the limited body of literature on energy optimization in elevators and emphasizes the importance of balancing energy efficiency with service quality. These findings provide guidance for establishing benchmarks in reducing energy consumption, in relation to elevator systems.

Taking into account the contributions of economic performance (GDP), urbanization (URB), industrial structure (IND), and renewable energy consumption (REC), this paper examines the impact of green technology innovation (GTE), energy efficiency (EF), and environmental regulation (ER) on CO2 emissions in Chinese provinces from 2010 to 2020. Using the GMM method for the initial estimation, the MMQR as 2nd generation test for robustness and innovative panel causality presented by the JKS test, we have found: 1) a one percent boom in GDP is linked with a 0.08% upward push in CO₂ emissions throughout 30 provinces in China. 2) the renewable energy and energy efficiency data seems to effectively decrease CO₂ emissions, with a more pronounced impact observed at the upper quantile. 3) The environmental policy is limited across all quantiles. The study examines novel implications regarding sustainable development and carbon neutrality objectives.

This perspective paper argues how a social network approach can contribute to creating a more comprehensive picture of how individual and community characteristics influence participation in community energy initiatives (CEIs). We argue how social network theory and methods for social network analysis can be utilized to better understand participation. Further, we show how this can potentially aid the implementation of interventions aimed at attracting more participants with more diverse socio-demographic backgrounds. Importantly, we argue that the structure of community social networks connecting (potential) participants could importantly influence whether and how individual and community properties affect CEI participation. Our aim is conveying the social network approach to the field of community energy researchers and stakeholders who might not be familiar with it. We discuss empirical evidence on the effect of network characteristics on CEI participation and the connection between research on CEIs and adjacent fields as a foundation for our claims. We also illustrate how a social network approach might help to overcome biased participation and low participation numbers, by providing social scientists with a tool to give empirically grounded advice to CEIs. We conclude by looking at avenues for future research and discuss how the context of CEIs might yield new theoretical insights and hypotheses.

As long-distance flights increase, the widespread use of electric heating for hot water in domestic civil aircraft will pose a challenge to the aircraft's energy systems. Moreover, the aircraft environmental control system operates under variable environmental conditions during aircraft take-off, leading to changes in system performance and outlet parameters. In this paper, mathematical models of the new aircraft environmental control system are established during aircraft take-off, and the main factors affecting the performance of systems are discussed. Results show that hot water with an average temperature of 61 °C can be provided by the new system during aircraft take-off. In the new multi-functional system, the bleed air supply volume during aircraft take-off is less than that of the conventional system, and the system energy loss is also less. When the aircraft just takes off, the condenser accounts for the most significant portion of the system exergy loss. However, the exergy loss in the secondary heat exchanger is the largest, as the aircraft altitude increases. Compared with the conventional system, the exergy efficiency of the new system is 8.85% higher at a 4-5 km level flight, and it’s 3.21% higher at a 9-10 km level flight.

Carbon-footprint reduction of key industrial buildings is addressed, by proposing methodologies for continuously monitoring telecommunication (TLC) central offices (COs). Main aim is classifying sites according to their efficiency and reliability, via the diagnosis of anomalous electricity consumptions. Such a goal is achieved through the definition of new key performance indicators (KPIs) based on TLC and cooling energy demand, improving the outcomes of pre-existing methods. While the reliability index and index of cluster reliability are specifically proposed to evaluate and physically assess the impact of climate control (CLC, i.e., the parasitic quota) electricity consumption with respect to the TLC one, the here introduced coefficient of variation of telecommunication energy allows for a more solid evaluation of energy measurements reliability. Another target of this study is to extend the afore-mentioned KPIs-based analysis to multi-annual periods of monitoring, thus allowing successfully meeting the currently in-force ISO 50001 standard. Specific central offices were then selected and analyzed to verify the results physical meaning. The method was proven effective in classifying central offices belonging to climate-homogenous fleets, according to the reliability level estimated over a triannual timeframe. Positive impacts in terms of attainable energy saving through improved thermal management, as well as methodology extendibility to other industrial sectors are finally presented and discussed.

It was last estimated in 2016 that data centers (DCs) comprise approximately 2% of total US electricity consumption. However, this estimate is currently being updated to account for the massive increase in computing needs due to streaming, cryptocurrency, and artificial intelligence (AI). To prevent energy consumption that tracks with increasing computing needs, it is imperative we identify energy efficiency strategies and investments beyond the low-hanging fruit solutions. In a two-phased research approach, we ask: What non-technical barriers still impede energy efficiency (EE) practices and investments in the data center sector, and what can be done to overcome these barriers? In particular, we are focused on social and organizational barriers to EE. In Phase I, we performed a literature review and found that technical solutions are abundant in the literature, but fail to address the top-down cultural shifts that need to take place in order to adapt new energy efficiency strategies. In Phase II, reported here, we interviewed 16 data center operators/experts to ground-truth our literature findings. Our interview protocols focus on three aspects of DC decision-making: procurement practices, metrics and monitoring, and perceived barriers to energy efficiency. We find that vendors are the key drivers of procurement decisions, advanced efficiency metrics are facility-specific, and there is convergence in the design of advanced facilities due to the heat density of parallelized infrastructure. Our ultimate goals for our research are to design DC decarbonization policies that target organizational structure, empower individual staff, and foster a supportive external market.