The household sector is one of the most energy-intensive sectors in Europe, and thus a focal point for reducing greenhouse gas emissions associated with energy consumption. Energy efficiency is ...considered a key measure to reduce household energy consumption, but several factors could lead to an underinvestment in energy efficiency. This is the so-called energy efficiency gap or paradox. The factors in question are grouped under market failures (including informational failures), behavioural failures and other factors. Various policies can be used to address these failures and promote the adoption of energy-efficient technologies, including energy standards and codes, economic incentives and information instruments. This paper reviews the empirical evidence to date on energy efficiency policies and discusses their effectiveness. On the one hand, command and control instruments seem to be effective policies, but they have to overcome several barriers. In the case of price instruments, subsidies and taxes do not seem to be effective while rebates present mixed results as they sometimes are effective and in other cases, they could present significant shortcomings. Finally, the effectiveness of informational policies is not always ensured as they depend on the country, sector and product category. Information feedback tools also seem to be effective as they work as a constant reminder of energy-efficient behaviour. Some limitations of energy efficiency policies are also identified, such as the difficulties of implementing codes and standards given that a minimum level need to be achieved, differences in the effectiveness of rebate programmes and non-conclusive results in regard to the effectiveness of monetary energy efficiency labels.
An analytical job creation model for the US power sector from 2009 to 2030 is presented. The model synthesizes data from 15 job studies covering renewable energy (RE), energy efficiency (EE), carbon ...capture and storage (CCS) and nuclear power. The paper employs a consistent methodology of normalizing job data to average employment per unit energy produced over plant lifetime. Job losses in the coal and natural gas industry are modeled to project net employment impacts. Benefits and drawbacks of the methodology are assessed and the resulting model is used for job projections under various renewable portfolio standards (RPS), EE, and low carbon energy scenarios We find that all non-fossil fuel technologies (renewable energy, EE, low carbon) create more jobs per unit energy than coal and natural gas. Aggressive EE measures combined with a 30% RPS target in 2030 can generate over 4 million full-time-equivalent job-years by 2030 while increasing nuclear power to 25% and CCS to 10% of overall generation in 2030 can yield an additional 500,000 job-years.
Recent studies indicate that houses with higher energy efficiency usually have higher market prices, a 'market premium for energy efficiency'. But in Germany the usefulness of this premium is ...confounded by the 'prebound effect': the gap between officially certificated energy ratings and actual energy consumption. Attempts have been made to close this gap from two complementary directions: downwards, by obtaining more accurate and less pessimistic technical estimates of idealised energy performance; and upwards, by estimating how much energy occupants realistically need for health and comfort. This study investigates prebound effects alongside an analysis of house prices in Germany, using a large database of house sale advertisements from 2007 to 2021, focusing on pre-1980 homes that were re-sold in 2019-2021. It uses ordinary least-squared multivariate regression to estimate market premiums for energy efficiency and sets these alongside estimates of prebound (and rebound) effects. It finds that prebound effects can lead purchasers to overestimate future energy savings and therefore pay more for properties than their actual worth. It also offers simple models to help purchasers interpret energy ratings more critically and estimate likely energy savings more realistically. Finally, it suggests how policymakers could modify energy ratings to reflect likely energy consumption more accurately.
Building energy efficiency has been a cornerstone of greenhouse gas mitigation strategies for decades. However, impact evaluations have revealed that energy savings typically fall short of ...engineering model forecasts that currently guide funding decisions. This creates a resource allocation problem that impedes progress on climate change. Using data from the Illinois implementation of the U.S.’s largest energy efficiency program, we demonstrate that a data-driven approach to predicting retrofit impacts based on previously realized outcomes is more accurate than the status quo engineering models. Targeting high-return interventions based on these predictions dramatically increases net social benefits, from $0.93 to $1.23 per dollar invested.
•ML-based prediction with household data can increase the accuracy of engineering projections used in the nation’s largest energy efficiency program.•We develop an ex ante targeting function to predict the NPV of benefits from a range of retrofits.•Targeting funds to projects using ex ante projections increases benefits from $0.93 to $1.23.
This paper presents a methodology for optimal design of diesel/PV/wind/battery hybrid renewable energy system (HRES) for the electrification of residential buildings in rural areas. Contrary to ...previous work, in this study, the effects of climate diversity and building energy efficiency on the size optimization of HRES are investigated. First, a multi-criteria spatial analysis trough a common geographical information system tool (ArcGIS 10.2) is undertaken to develop the renewable energy potential map for Algeria. Then, particle swarm optimization algorithm and ε-constraint method were used to solve the multi-objective problem, which was formulated to minimize the cost of energy (COE) as the primary objective, while maximizing system reliability and a renewable fraction (RF). According to the resulting renewable potential map, seven zones are identified, and then seven locations have been selected (one from each zone) to execute the optimization of the proposed HRES. By considering low efficient buildings, photovoltaic/wind/diesel/battery is found the best configuration for Adrar and Tindouf, while photovoltaic/diesel/battery is obtained the best for the other locations. However, in the case of high-performance buildings, another optimal HRES configurations are obtained. The better one is acquired in Biskra and Tamenrast, which includes PV-Battery (100% renewable energy) and fulfilling COE of 0.21 $/kWh.
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•Hybrid Renewable Energy Systems.•Techno-Economic and Feasibility Study.•Building Energy Efficiency.•Multi-Objective Particle Swarm Optimization.•Geographical information system.
Spiking neural networks (SNNs) are a large class of neural model distinct from ‘classical’ continuous-valued networks such as multilayer perceptrons (MLPs). With event-driven dynamics and a ...continuous-time model in contrast to the discrete-time model of their classical counterparts, they offer interesting advantages in representational capacity and energy consumption. Spiking networks may also be more biologically plausible, offering more insights into neuroscience. However, developing models of learning for SNNs has historically proven challenging: as discrete-time systems, their dynamics are much more complex and they cannot benefit from the strong theoretical developments in MLPs such as convergence proofs and optimal gradient descent. Nor do they gain automatically from algorithmic improvements that have produced efficient matrix inversion and batch training methods. Most of the existing research has focused on the most well-studied learning mechanism in SNNs, spike-timing-dependent plasticity (STDP), and although there has been progress, there are also notable pathologies that have often been solved with a variety of ad-hoc techniques. While efforts have been made to map SNNs to classical convolutional neural networks (CNNs), these have not yet shown any decisive efficiency advantage over conventional CNNs. More promising research directions lie in the realm of pure spiking learning models that exploit the inherent temporal dynamics (and often leverage recurrency). Metrics are needed; one possibility would be a measure of total energy cost per unit reduction in error. This tutorial overview looks at existing techniques for learning in SNNs and offers some thoughts for future directions.
•Tutorial on spiking neural networks (SNNs).•Recommendations for SNN implementors and researchers.•Current neural network challenges addressed by SNNs.•Metrics for evaluating Spiking Neural Network performance.
•Innovative and promising strategies to improve energy efficiency of a medium size cruise ship.•Detailed modifications to onboard engines layout are provided suggesting five different ...configurations.•Energetic, economic and environmental performance of proposed strategy are quantified.•Calculation code based on genetic algorithm has been developed and used to evaluate the performance.•For each investigated strategies optimum thermal storage has been identified.
Last years have been characterized by a worldwide increasing attention towards the reduction of fuel consumption and carbon dioxide emissions. Several industrial fields, as well as the civil and residential sector, have introduced innovative approaches for the design and the operation of energy systems. These actions are aimed to reach higher values of energy conversion efficiency, also including an increase in the use of renewable resources. In this context, especially in the sector of cruise ships, further efforts are required to improve the energy efficiency of the employed energy systems. The aim of this paper is to propose an optimization framework based on genetic algorithms in order to maximize the energy efficiency and minimize both the fuel consumption and the thermal energy dissipation, by optimizing the load allocation of the ship energy systems. To this purpose, different strategies for the energy systems on board of an existing cruise ship are proposed and analyzed. In particular, two main engines configurations have been defined: standard (current logic of operation maintained) and hybrid configuration. For each proposed strategy – being the ship a particular and interesting application of isolated energy grid (i.e. a grid without connections with electric and fuel national grids) – an in-house-developed software has been adapted and applied to optimize the load allocation of the various energy systems. Furthermore, an economic and environmental analysis has been carried out, in order to point out the benefits – or the eventual limits – related to the proposed solutions. The considered approach is based on the concept of introducing economically and structurally suitable modifications to the current cruise energy systems configuration, in order to reach the goal of increasing the energy efficiency. The carried out analysis shows that the hybrid strategies allow to reach the best results in terms of energy (fuel consumption and heat dissipation reduction), economic and environmental points of view.
Rubbery polymeric membranes, containing amine carriers, have received much attention in COsub.2 separation because of their easy fabrication, low cost, and excellent separation performance. The ...present study focuses on the versatile aspects of covalent conjugation of L-tyrosine (Tyr) onto the high molecular weight chitosan (CS) accomplished by using carbodiimide as a coupling agent for COsub.2/Nsub.2 separation. The fabricated membrane was subjected to FTIR, XRD, TGA, AFM, FESEM, and moisture retention tests to examine the thermal and physicochemical properties. The defect-free dense layer of tyrosine-conjugated-chitosan, with active layer thickness within the range of ~600 nm, was cast and employed for mixed gas (COsub.2/Nsub.2) separation study in the temperature range of 25−115 °C in both dry and swollen conditions and compared to that of a neat CS membrane. An enhancement in the thermal stability and amorphousness was displayed by TGA and XRD spectra, respectively, for the prepared membranes. The fabricated membrane showed reasonably good COsub.2 permeance of around 103 GPU and COsub.2/Nsub.2 selectivity of 32 by maintaining a sweep/feed moisture flow rate of 0.05/0.03 mL/min, respectively, an operating temperature of 85 °C, and a feed pressure of 32 psi. The composite membrane demonstrated high permeance because of the chemical grafting compared to the bare chitosan. Additionally, the excellent moisture retention capacity of the fabricated membrane accelerates high COsub.2 uptake by amine carriers, owing to the reversible zwitterion reaction. All the features make this membrane a potential membrane material for COsub.2 capture.
In today’s world, a significant amount of global energy is used in buildings. Unfortunately, a lot of this energy is wasted, because electrical appliances are not used properly or efficiently. One ...way to reduce this waste is by detecting, learning, and predicting when people are present in buildings. To do this, buildings need to become “smart” and “cognitive” and use modern technologies to sense when and how people are occupying the buildings. By leveraging this information, buildings can make smart decisions based on recently developed methods. In this paper, we provide a comprehensive overview of recent advancements in Internet of Things (IoT) technologies that have been designed and used for the monitoring of indoor environmental conditions within buildings. Using these technologies is crucial to gathering data about the indoor environment and determining the number and presence of occupants. Furthermore, this paper critically examines both the strengths and limitations of each technology in predicting occupant behavior. In addition, it explores different methods for processing these data and making future occupancy predictions. Moreover, we highlight some challenges, such as determining the optimal number and location of sensors and radars, and provide a detailed explanation and insights into these challenges. Furthermore, the paper explores possible future directions, including the security of occupants’ data and the promotion of energy-efficient practices such as localizing occupants and monitoring their activities within a building. With respect to other survey works on similar topics, our work aims to both cover recent sensory approaches and review methods used in the literature for estimating occupancy.
•The first city-level building energy efficiency benchmarking system for China.•A model considering the influence of operation parameters on energy consumption.•A systematic methodology of building ...energy benchmarking is proposed.•The model is a valuable analysis support tool for building energy managers.
The city-level public building energy efficiency benchmarking system has an important guiding value for building energy-saving work, especially for Beijing, with its high energy-consumption density and both long heating and air-conditioning periods. This paper analyzed the characteristics of large-scale office buildings in Beijing and gives a detailed description of the process of establishing a public building energy efficiency benchmarking system for large-scale office buildings in Beijing, China, including data investigation and verification, Energy Usage Intensity(EUI) calculation, determining independent and dependent variables, developing and testing the benchmarking model, and deriving the benchmarking rating table. Then the first city-level public office building energy efficiency benchmarking system in China is established based on 88 large-scale office buildings in Beijing. The results showed that the model's performance index R2 is 0.667 and gives a good representation. The process has a strong potential for replication for other cities in China and even for a national energy-consumption evaluation system.
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