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This study presents an innovative framework for leveraging the potential of AI in energy systems through a multidimensional approach. Despite the increasing importance of sustainable energy systems in addressing global climate change, comprehensive frameworks for effectively integrating artificial intelligence (AI) and machine learning (ML) techniques into these systems are lacking. The challenge is to develop an innovative, multidimensional approach that evaluates the feasibility of integrating AI and ML into the energy landscape, to identify the most promising AI and ML techniques for energy systems, and to provide actionable insights for performance enhancements while remaining accessible to a varied audience across disciplines. This study also covers the domains where AI can augment contemporary and future energy systems. It also offers a novel framework without echoing established literature by employing a flexible and multicriteria methodology to rank energy systems based on their AI integration prospects. The research also delineates AI integration processes and technique categorizations for energy systems. The findings provide insight into attainable performance enhancements through AI integration and underscore the most promising AI and ML techniques for energy systems via a pioneering framework. This interdisciplinary research connects AI applications in energy and addresses a varied audience through an accessible methodology.

Increasing renewable energy use is an essential strategy for mitigating climate change. Nevertheless, the sensitivity of renewable energy to climatic conditions means that the energy system’s vulnerability to climate change can also become larger. In this research, we used two integrated assessment models and data from four climate models to analyse climate change impacts on primary energy use at a global and regional scale under a low-level (RCP2.6) and a medium-level (RCP6.0) climate change scenario. The impacts are analysed on the energy system focusing on four renewable sources (wind, solar, hydropower, and biomass). Globally, small climate impacts on renewable primary energy use are found in both models (5% for RCP2.6 and 6% for RCP6.0). These impacts lead to a decrease in the use of fossil sources for most regions, especially for North America and Europe under the RCP60 scenario. Overall, IMAGE and GCAM provide a similar signal impact response for most regions. E.g. in Asia (excluding China and India), climate change induces an increase in wind and hydropower use under the RCP6.0 scenarios; however, for India, a decrease in solar energy use can be expected under both scenarios and models.

The bi-directional energy flow between prosumers (wind energy) and smart grid (SG) provides pertinent benefits, such as (i) load-sharing, (ii) peak-load shaving, (iii) load reduction with energy market programs, (iv) ancillary services-based energy transactions, and (v) mutual beneficial frameworks based on rewards and penalties. However, the load variations of SG, intermittent wind speed in energy district (ED) of prosumers, and stochastic energy price are the major constraints that must be considered in wind energy prosumers (WEPs) interaction with utility. Further, the interfacing and interactions of WEPs with SG incur an enormous volume of data to be processed, stored, accessed, and managed. Therefore, the authors proposed a stochastic bi-directional energy management model (BEMM) to manage the aforementioned constraints. Moreover, the BEMM is empowered with cloud-based service level agreement (C-SLA) that provides massive storage capabilities to the enormous data incurred due to WEPs interactions with SG. Two sub-models of BEMM are incorporated, namely stochastic wind estimation model and stochastic energy pricing model. The wind estimation model deals the stochasticity of wind speed for energy generation, while energy price model manages and controls the uncertainty of pricing tariffs based on real-time pricing and day-a-head pricing mechanisms for efficient energy trade between SG and WEPs under the principle of C-SLA.

This paper presents a simple hydrogen fuel cell vehicle (HFCV) energy consumption model. Simple fuel/energy consumption models have been developed and employed to estimate the energy and environmental impacts of various transportation projects for internal combustion engine vehicles (ICEVs), battery electric vehicles (BEVs), and hybrid electric vehicles (HEVs). However, there are few published results on HFCV energy models that can be simply implemented in transportation applications. The proposed HFCV energy model computes instantaneous energy consumption utilizing instantaneous vehicle speed, acceleration, and roadway grade as input variables. The mode accurately estimates energy consumption, generating errors of 0.86% and 2.17% relative to laboratory data for the fuel cell estimation and the total energy estimation, respectively. Furthermore, this work validated the proposed model against independent data and found that the new model accurately estimated the energy consumption, producing an error of 1.9% and 1.0% relative to empirical data for the fuel cell and the total energy estimation, respectively. The results demonstrate that transportation engineers, policy makers, automakers, and environmental engineers can use the proposed model to evaluate the energy consumption effects of transportation projects and connected and automated vehicle (CAV) transportation applications within microscopic traffic simulation models.

Reliable energy models are needed to determine building energy performance. Relatively detailed energy models can be auto-generated based on 3D shape representations of existing buildings. However, parameters describing thermal performance of the building fabric, the technical systems, and occupant behavior are usually not readily available. Calibration with on-site measurements is needed to obtain reliable energy models that can offer insight into buildings’ actual energy performances. Here, we present an energy model that is suitable for district-heated multifamily buildings, based on a 14-node thermal network implementation of the ISO 52016-1:2017 standard. To better account for modeling approximations and noisy inputs, the model is converted to a stochastic state-space model and augmented with four additional disturbance state variables. Uncertainty models are developed for the inputs solar heat gains, internal heat gains, and domestic hot water use. An iterated extended Kalman filtering algorithm is employed to enable nonlinear state estimation. A Bayesian calibration procedure is employed to enable assessment of parameter uncertainty and incorporation of regulating prior knowledge. A case study is presented to evaluate the performance of the developed framework: parameter estimation with both dynamic Hamiltonian Monte Carlo sampling and penalized maximum likelihood estimation, the behavior of the filtering algorithm, the impact of different commonly occurring data sources for domestic hot water use, and the impact of indoor air temperature readings.

Building Energy Models (BEMs) are a key element of the Energy Performance of Buildings Directive (EPBD), and they are at the basis of Energy Performance Certificates (EPCs). The main goal of BEMs is to provide information for building stakeholders; they can be a powerful market tool to increase demand for energy efficiency solutions in buildings without affecting the comfort of users, as well as providing other benefits. The next generation of BEMs should value buildings in a holistic and cost-effective manner across several complementary dimensions: envelope performances, system performances, and controlling the ability of buildings to offer flexible services to the grid by optimizing energy consumption, distributed generation, and storage. SABINA is a European project that aims to look for flexibility to the grid, targeting the most economic source possible: existing thermal inertia in buildings. In doing so, SABINA works with a new generation of BEMs that tend to mimic the thermal behavior of real buildings and therefore requires an accurate methodology to choose the model that complies with the requirements of the system. This paper details our novel extensive research on which statistical indices should be chosen in order to identify the best model offered by the calibration process developed by Fernandez et al. in a previous paper and therefore is a continuation of that work.

A proliferation of energy models has been developed across disciplines to explore energy and greenhouse gas (GHG) emissions-reduction strategies in cities. Hybrid models are especially useful because they incorporate more dynamics to simulate realistic results informed by relevant high-level policy decisions and building-level factors. Spatial and aspatial energy models, however, are not often linked, which overlooks the spatial impact of energy and emissions policies in urban environments. A new method is presented that links these types of models to understand how building stocks change over time in response to policies. This approach integrates outputs from an aspatial economic model, CIMS, with buildings in a spatially explicit urban building energy model (UBEM), UMI. The energy–economy model is parameterised against the UBEM using identified baseline condition and proposed future policy interventions. Building stock replacement and retrofit change are downscaled and disaggregated to individual buildings based on existing stock age and a probability-based Markov chain model (MCM). This integration enables simulations of cross-scale policy interventions that are sensitive to both economically and mechanically driven factors. An application of this approach shows how it can be used to evaluate how different policies interact with and influence building energy demand and GHG emissions.Practice relevanceThe results are integrated as a series spatially explicit energy modeling procedure (UMI) at the neighborhood scale. This process enables local assessments of efficacy of the proposed city scale and even regional policies in municipalities with various energy and GHG emission agendas. In the presented case study (of the Sunset neighborhood of Vancouver, BC, Canada) this method can quantify the elasticity of emission reductions from various urban form changes (e.g. infill, transportation-oriented development, etc.), new building code (i.e. BC Energy Step Code), active transportation and retrofit strategies from 2020 to 2050.

Building energy performance (BEP) is an ongoing point of reflection among researchers and practitioners. The importance of buildings as one of the largest activators in climate change mitigation was illustrated recently at the United Nations Framework Convention on Climate Change 21st Conference of the Parties (COP21). Continuous technological improvements make it necessary to revise the methodology for energy calculations in buildings, as has recently happened with the new international standard ISO 52016-1 on Energy Performance of Buildings. In this area, there is a growing need for advanced tools like building energy models (BEMs). BEMs should play an important role in this process, but until now there has no been international consensus on how these models should reconcile the gap between measurement and simulated data in order to make them more reliable and affordable. Our proposal is a new generation of models that reconcile the traditional data-driven (inverse) modelling and law-driven (forward) modelling in a single type that we have called law-data-driven models. This achievement has greatly simplified past methodologies, and is a step forward in the search for a standard in the process of calibrating a building energy model.

The development of materials for clean and efficient energy generation and storage is one of the most rapidly developing, multi-disciplinary areas of contemporary science, driven primarily by concerns over global warming, diminishing fossil-fuel reserves, the need for energy security, and increasing consumer demand for portable electronics. Computational methods are now an integral and indispensable part of the materials characterisation and development process.   Computational Approaches to Energy Materials presents a detailed survey of current computational techniques for the development and optimization of energy materials, outlining their strengths, limitations, and future applications.  The review of techniques includes current methodologies based on electronic structure, interatomic potential and hybrid methods. The methodological components are integrated into a comprehensive survey of applications, addressing the major themes in energy research. Topics covered include: • Introduction to computational methods and approaches • Modelling materials for energy generation applications: solar energy and nuclear energy • Modelling materials for storage applications: batteries and hydrogen • Modelling materials for energy conversion applications: fuel cells, heterogeneous catalysis and solid-state lighting • Nanostructures for energy applications This full colour text is an accessible introduction for newcomers to the field, and a valuable reference source for experienced researchers working on computational techniques and their application to energy materials.

Large eddy simulations of a three-dimensional (3D) compressible parallel jet flow at Mach number of 0.9 and Reynolds number 2000 are carried out. Four subgrid-scale (SGS) models, namely, the standard Smagorinsky model (SM), the selective mixed scale model (SMSM), the coherent-structure Smagorinsky model (CSM) and the coherent-structure kinetic-energy model (CKM) are employed, respectively, and compared. The purpose of the study is to compare the SGS models and to find their suitability of predicting the flow transition in the potential core of the jet, and so as to provide a reference for selecting SGS models in simulating compressible jet flows, which is a kind of proto-type flow in fluid dynamics and aeroacoustics. A finite difference code with fourth-order spatial and very low storage third-order explicit Runge-Kutta temporal schemes is introduced and employed for calculation. The code, which was previously designed for simulating shock/boundary-layer interactions and had been widely validated in simulating a variety of compressible flows, is rewritten and changed into parallelized using the OpenMP protocol so that it can be run on memory-shared multi-core workstations. The computational domain size and the index of LES resolution quality are checked to validate the simulations. Detailed comparisons of the four SGS models are carried out. The results of averaged flow-field including the velocity profiles and the developments of shear-layer, the instantaneous vortical flows and the viscous dissipation, the predicted turbulence statistics and the balances of momentum equation are studied and compared. The results show that although the normalized developed velocity profiles are well predicted by the four SGS models, the length of the potential core and the development of the shear-layer reveal that the SM has excessive SGS viscosity and is therefore too dissipative to correctly predict the flow transition and shear-layer expansion. The model smears small vortical scales and lowers down the effective Reynolds number of the flow because of the over-predicted SGS viscosity and dissipation. The turbulence statistics and the balances of momentum equation have also confirmed the excessive dissipation of the SM. The CKM is also found to over-predict the SGS viscosity. Compared with these two models, the SMSM and the CSM have performed well in predicting both the averaged and the instantaneous flow-fields of the compressible jet. And they are localized models which are computationally efficient and easy for coding. Therefore, the SMSM and the CSM are recommended for the LES of the compressible Jet.