Real Energy Research Articles

Energy consumption of UAE public schools. Mapping of a diversified sector assessing typology, conditions, and educational systems

The UAE school sector impacts several Sustainable Development Goals of the UAE Vision 2021. The sector is designed according to a standardized system of Model buildings, repeated several times throughout the country, comprising facilities that are often obsolete at both functional and technological level; hence, frequently presenting poor energy performances. The paper presents the first comprehensive review of the energy consumption state-of-the-art of the public-school sector in the Emirate of Sharjah, United Arab Emirates, including the first necessary dataset for future upgrade actions. The study was completed by means of a deep mapping, functional, typological, and pedagogical analysis. The collected data of real energy consumption of 69 schools highlighted an average energy consumption of 174 kWh/m2 year, with very consistent results for each educational cycle, even if a larger variance was expected due to difference in size, typology, and working hour. More significant differences can be observed when comparing the result of school with different typologies, stressing the relevance of further investigating the standardized Model system. To quantify their correlation to the buildings’ energy consumption, linear regression analyses were conducted on a number of notable factors, such as building age, net floor area, roof area, shape coefficient, number of students, number of classrooms and labs, presence of gyms and multipurpose halls, and implementation of digital technologies. Schools’ capacity/occupancy ratio, net floor area and roof area, and the use of energy-demanding devices for novel smart learning techniques were found having the highest impact. The tested multiple regression model, which displays a marginal error of 5–30 % when applied to single buildings and 9 % when used to assess larger clusters, could facilitate the estimation of the energy consumption of schools and support stakeholders’ decision making. The outcomes of the study can assist governmental agencies in elaborating suitable refurbishment policies for the energy and functional upgrade of the educational sector, both at the building and urban scale, and school administrator in tackling appropriate actions toward the schools use and resources optimization.

Deep retrofitting toward net zero energy building in the Mediterranean region: A case study of Albania

Like many other Mediterranean countries, Albania faces unique challenges and opportunities to achieve an efficient and fully decarbonized household sector by applying real energy efficiency measures and numbers toward nearly zero-energy buildings. The study findings showed that nZEB in 2030 can be achieved by combining active and passive energy efficiency measures. Behind the study's state-of-the-art stands a multivariable regression analysis of both electricity consumption and electricity generation executed for three validated consecutive years, 2021, 2022, and 2023, including demand side (electricity bills) and supply side generation provided from a nearby existing onsite PV with an installed capacity of 5.5 kWp. A good mathematical correlation is carried out using Durbin – Watson criteria on statistics using RETScreen software to correlate electricity consumption and generation as a function of weather parameters for the tested household and selected region. The goals to meet the nZEB concept require the insulation of the existing exterior wall and window replacements in compliance with the Energy Performance Methodology on Building and installing rooftop Solar Water Heating (SWH) panels of 3.73 m2 as expostulated in scenario 7. The simulation results show that integrating a rooftop PV system with an installed capacity of 5.5 kWp would provide enough electricity to convert residential buildings into Positive Energy Buildings in 2050 based on the selected dwelling and location (Mediterranean region). At the national level, the proposed model would reduce the national annual electricity import level by 1.64 %, saving annually around €31,021,162 and reducing at least 0.068 MtCO2 per year. Subsidizing passive energy efficiency measures at a rate of 3 % per year coupled with onsite renewable energy sources schemes and other incentivization pathways from the government and other interested parties at a rate of 80 % in the roadmap towards nZEB and PEB is mandatory.

A simulation of diesel and biodiesel blending for energy generation

This study is to simulate bioenergy in the Saudi energy sector. The methodologies used in this study include the simulation of biodiesel blends in power plants. GIS software was used to calculate the area of Jatropha plants cultivated (228.5 km2) to produce 1.62 kg/s Jatropha oil. The results showed that the validation was done by comparing the net power output (72.3 MW) and efficiency of 30.44% of the real energy plant with the simulation net power output of 69.15 MW and efficiency of 30.67%. The efficiency was increased to 51.1% after the simple cycle was modified to the combined cycle. Biodiesel (1.534 kg/s) was blended with diesel. The blended diesel and biodiesel were examined and the effect of the blends on the combined cycle power plant performance was inspected. The overall result revealed that the energy from biodiesel can be an alternative to support the Saudi Arabia Vision 2030.

Analysis of aggregated load consumption forecasting in short, medium and long term horizons using Dynamic Mode Decomposition

In response to the growing need for precise energy demand forecasting in distribution grids, this study examines the effectiveness of the Dynamic Mode Decomposition (DMD) algorithm for short, medium, and long-term demand forecasting in distribution grids, leveraging diverse time series energy consumption data. The DMD algorithm, a data-driven approach renowned for its capacity to discern frequencies and trends, will be compared against established forecasting techniques, including XGBoosting Regressor, Long Short-Term Memory, and PROPHET. The research is conducted through a comprehensive case study comprising three distinct phases: study on pattern capturing of DMD, comparison in Long term horizon with all models studied and robustness study of DMD. It recalls upon real aggregated hourly energy consumption data sourced from a Spanish distribution grid. The practical applications and benefits of DMD in time series forecasting are examined carefully in order to show its comparative advantages over alternative forecasting models. To ensure a rigorous assessment of the findings, the analysis will employ four key statistical metrics: Root Mean Square Error (RMSE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results obtained with the case studies showed a 75% reduction on computational waste compared to other models and the results are closer to classical models, obtaining an improvement in the best case study of 44% comparing RMSE with Prophet. The insights gained can help future applications and research in the field.

Federated continual representation learning for evolutionary distributed intrusion detection in Industrial Internet of Things

As a promising paradigm, Federated Learning (FL)-based distributed intrusion detection offers potent protection for the network security of Industrial Internet of Things (IIoT) systems. Nonetheless, the practical deployment of IIoT systems occurs in a highly-complex and dynamic distributed environment. The ever-growing and dynamically-evolving cyber attacks will render the FL-based intrusion detection model inefficient, since FL cannot gracefully learn from the dynamic traffic data streams to identify new attacks. To address this issue, we propose the evolutionary distributed intrusion detection system based on federated continual representation learning, designed to continually capture effective feature representations of emerging attacks from dynamic traffic data streams. Specifically, we develop the supervised contrastive loss and the global information-aware regularization loss to alleviate the catastrophic forgetting on the previously observed attacks and mitigate the data heterogeneity across clients. Besides, we propose the prototype variance-based memory update strategy to ensure the effective memory replay data. Extensive experimental results demonstrate our proposed method outperforms the state-of-the-art methods by 13.3%–31.5% in terms of average accuracy on a real energy intrusion detection dataset.

CoN//Co9S8 co-embedded in reduced graphene oxide as catalyst for catalytic conversion of polysulfides

Lithium-sulfur (Li–S) batteries with high theoretical specific capacity of 1675 mAh g−1 and energy density of 2600 Wh kg−1 are considered as a promising candidate for energy conversion and storage devices in future. However, some critical dilemmas such as low real energy density, high solubility of ether-based electrolytes and slow redox kinetics of long-chain polysulfides remain unsettled hindering their development. Here we design and propose a high-performance sulfur host material constructed by growing CoN/Co9S8 nanoparticles enclosed in reduced graphene oxide (RGO) and Co9S8 modified separator to enhance the electrochemical performance of Li–S batteries. The results of first principal calculations confirm that the calculated adsorption energies of Li2S8, Li2S6, Li2S4 and Li2S2 on the Co9S8 (111) surface are lower than that of the CoN (111) surface, indicating that the Co9S8 has stronger adsorbing effect with polysulfides. Furthermore, the catalytic effect in Li2S6 symmetric batteries and the nucleation of the Li2S tests display that the CoN/Co9S8@RGO has a fast conversion reaction kinetics and catalytic effect of polysulfides, thereby inhibiting the leakage of the polysulfides. Consequently, the S@CoN/Co9S8@RGO cathode with Co9S8 layer represents a high discharge capacity of 1367.7 mAh g−1 at 0.1C and a prolonged lifespan over 700 cycles at 1 C with an extremely low decay rate of 0.06 % per cycle and high cycling stability.

Dynamic verification of an optimisation algorithm for power dispatch of integrated energy systems

The urgent need to achieve net-zero carbon emissions by 2050 has led to a growing focus on innovative approaches to producing, storing, and consuming energy. Integrated energy systems (IES) have emerged as a promising solution, capitalising on synergies between energy networks and enhancing efficiency. Such a holistic approach enables the integration of renewable energy sources and flexibility provision from one energy network to another, reducing emissions while facilitating strategies for operational optimisation of energy systems. However, emphasis has been mostly made on steady-state methodologies, with a dynamic verification of the optimal solutions not given sufficient attention. To contribute towards bridging this research gap, a methodology to verify the outcomes of an optimisation algorithm is presented in this paper. The methodology has been applied to assess the operation of a civic building in the UK dedicated to health services. This has been done making use of real energy demand data. Optimisation is aimed at improving power dispatch of the energy system by minimising operational costs and carbon emissions. To quantify potential discrepancies in power flows and operational costs obtained from the optimisation, a dynamic model of the IES that better captures real-world system operation is employed. By incorporating slow transients of thermal systems, control loops, and non-linearity of components in the dynamic model, often overlooked in traditional optimisation modules, the methodology provides a more accurate assessment of energy consumption and operational costs. The effectiveness of the methodology is assessed through model-in-the-loop co-simulations between MATLAB/Simulink and Apros alongside a series of scenarios. Results indicate significant discrepancies in power flows and operational costs between the optimisation and the dynamic model. These findings illustrate potential limitations of conventional operational optimisation modules in addressing real-world complexities, emphasising the significance of dynamic verification methods for informed energy management and decision-planning.

The real reasons energy psychology is proving to be durable: Rejoinder to “Acupressure in psychotherapy as an unsinkable rubber duck, reply to Feinstein (2023)”.

The real reasons energy psychology is proving to be durable: Rejoinder to “Acupressure in psychotherapy as an unsinkable rubber duck, reply to Feinstein (2023)”.

Study on voltage consistency characteristics of lithium-ion battery energy storage system

Abstract In the long-term operation of lithium-ion battery energy storage power stations, the consistency of batteries, as an important indicator representing the operation condition of the system, needs to be focused. In practice, the parameters of voltage, capacity, and internal resistance are most commonly used for the consistency evaluation of batteries, and voltage is the most intuitive one. In this paper, a real lithium-ion battery energy storage power station is studied, and the consistency of voltages is calculated. The results show that, with the decline of the battery capacity during discharge, the consistency of the battery presents a strong nonlinear characteristic. At the beginning of discharge, the max-min and standard deviation of voltage are only 0.04 and 0.01 V, while at the end of discharge, the max-min and standard deviation of voltage are up to 0.25 V and 0.045 V, respectively. At about 40-50 minutes of the discharge, i.e., when the corresponding voltages are about 3.132 V to 3.176 V, the voltage consistency deteriorates rapidly. Then, this period of discharge is considered as a threshold interval of consistency deterioration of batteries. The results are helpful for system condition evaluation and operation optimization.

Energy Complexity of Convolutional Neural Networks.

The energy efficiency of hardware implementations of convolutional neural networks (CNNs) is critical to their widespread deployment in low-power mobile devices. Recently, a number of methods have been proposed for providing energy-optimal mappings of CNNs onto diverse hardware accelerators. Their estimated energy consumption is related to specific implementation details and hardware parameters, which does not allow for machine-independent exploration of CNN energy measures. In this letter, we introduce a simplified theoretical energy complexity model for CNNs, based on only a two-level memory hierarchy that captures asymptotically all important sources of energy consumption for different CNN hardware implementations. In this model, we derive a simple energy lower bound and calculate the energy complexity of evaluating a CNN layer for two common data flows, providing corresponding upper bounds. According to statistical tests, the theoretical energy upper and lower bounds we present fit asymptotically very well with the real energy consumption of CNN implementations on the Simba and Eyeriss hardware platforms, estimated by the Timeloop/Accelergy program, which validates the proposed energy complexity model for CNNs.

Semi-stationary and dynamic simulation models: A critical comparison of the energy and economic savings for the energy refurbishment of buildings

Dynamic simulation is a powerful tool for accurately evaluating the thermal demands of buildings and assessing the impact of energy refurbishment actions on their final consumption. Conversely, semi-stationary models are widely adopted in commercial applications for its simplified approach, which reduce calculation times, resulting in standardized results showing a certain deviation with respect to the real energy This paper presents the energy and economic comparison between the dynamic simulation and semi-stationary approaches for the calculation of primary energy demand of residential buildings. The semi-stationary method, used by the legislation to calculate the buildings energy label, is based on an energy performance parameter, not representative of the real energy demand. Conversely, an approach based on dynamic simulation provides a more reliable estimation of the primary energy demand. The main novelty of this paper is to numerically prove that the energy and economic savings calculated by means of software based on the current legislation may be overestimated. In this work, the dynamic simulation of the building-plant system is performed by TRaNsient SYstem Simulation (TRNSYS) program. Each building apartment is divided in thermal zones, where the internal heat gains are defined in detail. The semi-stationary simulation of the building-plant system is performed according to the Italian standard UNI TS 11300. The models allow one to evaluate the yearly primary energy demand, along with the energy bill and CO2 emissions. A specific case study is developed for a residential building located in Naples (Italy). The models are used to calculate the building energy demand for several scenarios, considering different thermal transmittances of the building elements. The results show that the semi-stationary method overestimates of primary energy saving, equal to 64.7 %, with respect to the one calculated with the dynamic approach, equal to 43.2 %.

Optimal integration and planning of PV and wind renewable energy sources into distribution networks using the hybrid model of analytical techniques and metaheuristic algorithms: A deep learning-based approach

The optimal integration of Renewable Energy Sources (RESs) into distribution networks is crucial for sustainable energy generation. However, existing approaches utilized for optimal RES integration, including metaheuristic and analytical methods, exhibit limitations. Analytical methods are simple to implement, computationally efficient, and able to model power system mechanisms, yet they face scalability issues, making them inefficient for large-scale distribution systems. On the other hand, metaheuristic approaches, while flexible for complex problems, suffer from premature convergence problems, limiting their application in real distribution networks. To address these issues, this paper introduces a novel hybrid model that combines the strengths of analytical and metaheuristic techniques. The proposed model employs a deep learning-based method called bi-directional long short-term memory (B-LSTM) network is utilized to handle the uncertainties associated with the Photovoltaic (PV) and Wind Turbine (WT) generation and load demand. Subsequently, a distinctive analytical multi-objective index-based procedure is developed to minimize the real and reactive energy loss and voltage deviation by analyzing the branch current change caused by the RES penetration in distribution networks. The optimization problem is then solved using the binary particle swarm optimization (BPSO) technique. Importantly, the presented method considers the long-term nature of optimal RES integration and reflects the seasonal impacts of the load and RES output powers. The proposed approach is applied to the IEEE 33-bus test system. The simulation results show that the introduced hybrid model, by addressing the limitations of existing approaches, offers a promising solution for efficient and effective RES integration in distribution systems while requiring low computational effort, thereby advancing the transition towards sustainable energy infrastructure.

Multi-agent cooperative optimal scheduling strategy of integrated energy system in urban area under extreme events

Because the global climate change intensifies as well as the natural disasters frequently occur, extreme events have caused serious impacts on the energy system in urban areas, and at the same time, they have brought great challenges to the supply and scheduling of urban energy systems. Therefore, in order to better integrate and manage various energy resources in urban areas, a Deep Q-Leaning Network-Quasi Upper Confidence Bound model is innovatively constructed using deep reinforcement learning technology to learn the state and behavior mapping relationship of energy system. Use deep learning to fit complex nonlinear models to optimize the entire energy system. Compare and verify the experiment with the real energy system. The improved Deep reinforcement learning algorithm is compared with Q-learning model, PDWoLF PHC algorithm model, Quasi Upper Confidence Bound algorithm model and deep Q-Leaning Network algorithm model. The results show that the research algorithm has the smallest instantaneous error value and absolute value of frequency deviation for area control, and the average value of the research algorithm in the absolute value of the frequency deviation is reduced by 45%–73% compared to other algorithms; over time, the unit output power of the research algorithm is able to flexibly track the stochastic square wave loads. Therefore, the proposed system strategies can provide feasible solutions to meet the challenges of extreme events and promote the sustainable development and safe operation of urban energy systems.

Challenges to energy retrofitting of existing office buildings in high-rise high-density cities: The case of Hong Kong

Achieving carbon neutrality by 2050 has become a global goal, sparking concerns regarding energy consumption and carbon emissions in building operations. Office buildings in high-rise high-density cities serve as central business districts, contributing significantly to the city’s economic activity and consuming a lot of energy. The process of retrofitting existing office buildings for energy efficiency in high-rise high-density cities tends to be challenging. However, there is a lack of comprehensive understanding of the challenges involved in office buildings’ energy retrofitting, as they have not been thoroughly explored. This study aims to investigate the challenges to the existing office building energy retrofitting (EOBER) in high-rise high-density cities with real cases in Hong Kong. Initially, a systematic literature review was undertaken to identify 49 potential EOBER challenges and categorized into seven groups: technical, financial, institutional, social, environmental, regulatory, and other categories. Afterward, 23 EOBER challenges were identified through 24 semi-structured interviews with 36 real office building energy retrofitting cases in Hong Kong. Moreover, these challenges were quantified by the Z-numbers-based Delphi survey and analysis. Results show that regulatory challenges are the primary challenges, followed by financial challenges. The lack of government incentives, policies, legislation and regulations significantly hinders practitioners’ ability to engage in energy retrofitting initiatives. The long payback period of building energy retrofitting poses a critical financial concern for practitioners embracing such initiatives. In the end, this research proposed integrated strategies to tackle these challenges and increase building energy efficiency, including launching financial and regulatory incentives, shortening the interval for mandatory energy audits, disseminating knowledge, and diversifying finance channels of building energy retrofitting. The findings contribute to the body of knowledge by employing systems thinking to identify and evaluate EOBER challenges in high-rise high-density cities through empirical methodologies. Moreover, this study provides valuable references for practitioners in navigating these challenges and minimizing risks associated with the retrofitting process.

Multi-source domain generalization deep neural network model for predicting energy consumption in multiple office buildings

The effective prediction of building energy consumption can be used to optimize building operating modes and reduce the overall energy consumption and carbon emission of the building. To predict the energy consumption of the same type of building in the same area, it is often necessary to train separate prediction models for different buildings, which is usually costly computationally in order to get better prediction results. Therefore, this study will combine deep neural networks and multi-source domain generalization in an encoder-decoder architecture to train a model that can be directly applied to predict the energy consumption of multiple buildings in same type. In order to validate the predictive effectiveness of the model, it will be tested on real office building energy consumption dataset and different comparative experiments will be designed on the source and target domains. The results show that the constructed multi-source domain generalization model is able to accurately predict the energy consumption trend of different buildings 1 h in advance. It also has some energy consumption prediction ability for the unknow training set of buildings.

Wearable Sensors and the Evaluation of Physiological Performance in Elite Field Hockey Players.

Sports performance tracking has gained a lot of interest and widespread use in recent years, especially in elite and sub-elite sports. This makes it possible to improve the effectiveness of training, to calibrate and balance workloads according to real energy expenditure, and to reduce the likelihood of injuries due to excessive physical stress. In this context, the aim of this review was to map the scientific literature on wearable devices used in field hockey, evaluating their characteristics and the available evidence on their validity in measuring physiological and movement parameters. A systematic investigation was carried out by employing five electronic databases and search terms that incorporated field hockey, wearables, and performance analysis. Two independent reviewers conducted assessments of the 3401 titles and abstracts for inclusion, and at the end of the screening process, 102 full texts were analyzed. Lastly, a total of 23 research articles that specifically concentrated on field hockey were incorporated. The selected papers dealt with performance monitoring (6 papers), technical analysis and strategy game (6), injury prevention (1), and physiological measurements (10). To appraise the quality of the evaluations, the Oxford quality scoring system scale was employed. The extraction of information was carried out through the utilization of the participants, intervention, comparison, and outcomes (PICOS) format. The analysis encompassed research studies that implemented wearable devices during training and competitive events. Among elite field hockey competitions, GPS units were identified as the predominant wearable, followed by heart rate monitors. The intraclass correlation coefficient (ICC) related to wearable devices showed reasonably high between-trial ICCs ranging from 0.77 to 0.99. The utilization of wearable devices in field hockey primarily centers around the measurement of player activity profiles and physiological demands. The presence of discrepancies in sampling rates and performance bands makes it arduous to draw comparisons between studies. Nevertheless, this analysis attested to the fact that wearable devices are being employed for diverse applications in the realm of field hockey.

IT-Framework for Digital Energy Twin/Shadow Applications

Digital Energy Twins are IT systems, interconnecting sensor data, simulation models and user interfaces to formulate a virtual representation of the behavior of real energy systems. Digital Energy Twins are useful to predict the behavior of energy systems under varying boundary conditions and to optimize their operation considering economic and ecologic impact. Two different concepts of Digital Twins applicable to industrial energy systems were demonstrated: Digital Energy Twins and Digital Energy Shadows. While in literature, the term “Digital Twin” is widely used as synonym, for rather different applications involving simulations and virtual models connected to real-world data, this paper elaborates on the differences between digital twins and digital shadows in more detail. Given by the complexity of real-world energy systems (heat and electricity) and their implications on real time simulation, the concepts are demonstrated on different TRL levels. The results show the benefits and limitations of Digital Energy Twin and Digital Energy Shadow applications in relevant environments.

Experimental method for estimating the effect of solar radiation on the inner surface heat flux of opaque building envelope elements

There still exists a considerable difference when comparing the real and the design energy consumption of buildings. The difference between the design and the real building envelope energy performance is one of its main reasons. The building envelope can be characterised through the individual characterisation of its different building envelope components such as opaque walls or windows. Therefore, the estimation of parameters such as their transmission heat transfer coefficient (UA) and their solar aperture (gA) is usually implemented. Although building components have been analysed over the years, the thermal characteristics of buildings have mainly been estimated through steady-state laboratory tests and simplified calculation/simulation procedures based on theoretical data. The use of inverse modelling based on registered dynamic data has also been used; however, unfortunately, the models used tend to significantly simplify or neglect the solar radiation effect on the inner surface heat flux of opaque building envelope elements. Therefore, this work presents an experimental, dynamic and inverse modelling method that accurately models non-linear phenomena through the use of a user-friendly simulation programme (LORD). The method is able to analyse in detail the effect of the solar radiation on the inner surface heat flux of opaque building envelope elements, without the necessity of knowing their constructive details or thermal properties. The experiment is performed in a fully monitored test box, where different models are tested with different opaque walls to find the best fit. Finally, the solar irradiance signal is removed from the best models so as to accurately quantify the weight of the solar radiation on the inner surface heat flux of each wall for two extreme periods, one for sunny summer days and other for cloudy winter days.

Innovative hybrid prediction method integrating wavelet threshold decomposition and entropy-based model selection strategy for building energy consumption prediction

Building energy prediction plays a pivotal role in the building energy management system, and an efficient prediction method is crucial to ensure the effective operation of building systems. Existing research enhances prediction accuracy by combining decomposition algorithms with prediction methods to separate non-stationary information from the original sequence. However, the decomposition process and the subsequent prediction of multiple decomposed subsequences result in significant time overhead, making it unsuitable for scenarios with high temporal requirements, such as short-term energy consumption forecasting. Additionally, a single prediction model struggles to adapt to the diverse features of decomposed subsequences, creating prediction accuracy and efficiency bottlenecks. Addressing these challenges, this study proposes a hybrid prediction method that combines wavelet threshold decomposition (WTD) with an entropy-based model selection strategy. The method initially utilizes the efficient WTD algorithm to decompose the original energy consumption data into three fixed subsequences. Subsequently, the permutation entropy for each subsequence is computed, and based on the comprehensive performance of four prediction models (ANN, LSTM, CNN-LSTM, and TCN) on sequences with different entropy values, the optimal prediction model is selected. Finally, the prediction results of the subsequences are aggregated to obtain the ultimate prediction outcome. Experimental results demonstrate that the proposed method significantly outperforms baseline methods in both prediction speed and accuracy across five real building energy consumption datasets with distinct operational modes.

Quantization of second-order fermions

The quantization of a massive spin-1/2 field that satisfies the Klein-Gordon equation is studied. The framework is consistent, provided it is formulated as a pseudo-Hermitian quantum field theory by the redefinition of the dual field and the identification of an operator that modifies the inner product of states in Hilbert space to preserve a real energy spectrum and unitary evolution. Since the fermion field has mass dimension one, the theory admits renormalizable fermion self-interactions. Published by the American Physical Society 2024