Search results for: energy models

Authors: Etidel Labidi

Abstract:

Energy efficiency improvement is one of the means to reduce energy consumption and carbon emissions. Recently, some developed countries have implemented the tradable white certificate scheme (TWC) as a new policy instrument based on market approach to support energy efficiency improvements. The major focus of this paper is to compare the White Certificates (TWC) scheme as an innovative policy instrument for energy efficiency improvement to other policy instruments: energy taxes and regulations setting a minimum level of energy efficiency. On the basis of our theoretical discussion and numerical simulation, we show that the white certificates system is the most interesting policy instrument for saving energy because it generates the most important level of energy savings and the least increase in energy service price.

Keywords: energy savings, energy efficiency, energy policy, white certificates

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Authors: Francis Okodede, Edafe Lucky Okotie

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Conventional energy sources based on oil, coal, and natural gas has posed a trait to environment and to human health. Green energy stands as an alternative because it has proved to be eco-friendly. The prospective of renewable energy sources are quite vast as they can, in principle, meet many times the world’s energy demand. Renewable energy sources, such as wind and solar, can provide sustainable energy services based on the use of routinely available indigenous resources. New renewable energy sources (solar energy, wind energy, and modern bio-energy) are currently contributing immensely to global energy demand. A number of studies have shown the potential and contribution of renewable energy to global energy supplies, indicating that in the second half of the 21st century, it is going to be a major source and driver in the telecommunication sector. Green energy contribution might reach as much as 50 percent of global energy demands if the right policies are in place. This work suggests viable non-conventional means of energy supply to power a cellular base station.

Keywords: base station, energy storage, green energy, rotor efficiency, solar energy, wind energy

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Authors: T. Lazzarini, M. Imbiki, P. E. Sutter, G. Borragan

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While several strategies, such as the development of more efficient appliances, the financing of insulation programs or the rolling out of smart meters represent promising tools to reduce future energy consumption, their implementation relies on people’s decisions-actions. Likewise, engaging with consumers to reshape their behavior has shown to be another important way to reduce energy usage. For these reasons, integrating the human factor in the energy transition has become a major objective for researchers and policymakers. Digital education programs based on tangible and gamified user interfaces have become a new tool with potential effects to reduce energy consumption4. The B2020 program, developed by the firm “Économie d’Énergie SAS”, proposes a digital platform to encourage pro-environmental behavior change among employees and citizens. The platform integrates 160 eco-behaviors to help saving energy and water and reducing waste and CO2 emissions. A total of 13,146 citizens have used the tool so far to declare the range of eco-behaviors they adopt in their daily lives. The present work seeks to build on this database to identify the potential impact of adopted energy-saving behaviors (n=62) to reduce the use of energy in buildings. To this end, behaviors were classified into three categories regarding the nature of its implementation (Eco-habits: e.g., turning-off the light, Eco-actions: e.g., installing low carbon technology such as led light-bulbs and Home-Refurbishments: e.g., such as wall-insulation or double-glazed energy efficient windows). General Linear Models (GLM) disclosed the existence of a significantly higher frequency of Eco-habits when compared to the number of home-refurbishments realized by the platform users. While this might be explained in part by the high financial costs that are associated with home renovation works, it also contrasts with the up to three times larger energy-savings that can be accomplished by these means. Furthermore, multiple regression models failed to disclose the expected relationship between energy-savings and frequency of adopted eco behaviors, suggesting that energy-related practices are not necessarily driven by the correspondent energy-savings. Finally, our results also suggested that people adopting more Eco-habits and Eco-actions were more likely to engage in Home-Refurbishments. Altogether, these results fit well with a growing body of scientific research, showing that energy-related practices do not necessarily maximize utility, as postulated by traditional economic models, and suggest that other variables might be triggering them. Promoting home refurbishments could benefit from the adoption of complementary energy-saving habits and actions.

Keywords: energy-saving behavior, human performance, behavioral change, energy efficiency

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Authors: Saeidreza Radpour, Md. Alam Mondal, Amit Kumar

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Market penetration of high energy efficiency technologies has key impacts on energy consumption and GHG mitigation. Also, it will be useful to manage the policies formulated by public or private organizations to achieve energy or environmental targets. Energy intensity in residential sector of Alberta was 148.8 GJ per household in 2012 which is 39% more than the average of Canada 106.6 GJ, it was the highest amount among the provinces on per household energy consumption. Energy intensity by appliances of Alberta was 15.3 GJ per household in 2012 which is 14% higher than average value of other provinces and territories in energy demand intensity by appliances in Canada. In this research, a framework has been developed to analyze the market penetration and market share of high energy efficiency technologies in residential sector. The overall methodology was based on development of data-intensive models’ estimation of the market penetration of the appliances in the residential sector over a time period. The developed models were a function of a number of macroeconomic and technical parameters. Developed mathematical equations were developed based on twenty-two years of historical data (1990-2011). The models were analyzed through a series of statistical tests. The market shares of high efficiency appliances were estimated based on the related variables such as capital and operating costs, discount rate, appliance’s life time, annual interest rate, incentives and maximum achievable efficiency in the period of 2015 to 2050. Results show that the market penetration of refrigerators is higher than that of other appliances. The stocks of refrigerators per household are anticipated to increase from 1.28 in 2012 to 1.314 and 1.328 in 2030 and 2050, respectively. Modelling results show that the market penetration rate of stand-alone freezers will decrease between 2012 and 2050. Freezer stock per household will decline from 0.634 in 2012 to 0.556 and 0.515 in 2030 and 2050, respectively. The stock of dishwashers per household is expected to increase from 0.761 in 2012 to 0.865 and 0.960 in 2030 and 2050, respectively. The increase in the market penetration rate of clothes washers and clothes dryers is nearly parallel. The stock of clothes washers and clothes dryers per household is expected to rise from 0.893 and 0.979 in 2012 to 0.960 and 1.0 in 2050, respectively. This proposed presentation will include detailed discussion on the modelling methodology and results.

Keywords: appliances efficiency improvement, energy star, market penetration, residential sector

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Authors: Mathieu Bourdeau, Philippe Basset, Julien Waeytens, Elyes Nefzaoui

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As residential buildings account for a third of the overall energy consumption and greenhouse gas emissions in Europe, building energy modeling is an essential tool to reach energy efficiency goals. In the energy modeling process, calibration is a mandatory step to obtain accurate and reliable energy simulations. Nevertheless, the comparison between simulation results and the actual building energy behavior often highlights a significant performance gap. The literature discusses different origins of energy performance gaps, from building design to building operation. Then, building operation description in energy models, especially energy usages and users’ behavior, plays an important role in the reliability of simulations but is also the most accessible target for post-occupancy energy management and optimization. Therefore, the present study aims to discuss results on the calibration ofresidential building energy models using real operation data. Data are collected through a sensor network of more than 180 sensors and advanced energy meters deployed in three collective residential buildings undergoing major retrofit actions. The sensor network is implemented at building scale and in an eight-apartment sample. Data are collected for over one year and half and coverbuilding energy behavior – thermal and electricity, indoor environment, inhabitants’ comfort, occupancy, occupants behavior and energy uses, and local weather. Building energy simulations are performed using a physics-based building energy modeling software (Pleaides software), where the buildings’features are implemented according to the buildingsthermal regulation code compliance study and the retrofit project technical files. Sensitivity analyses are performed to highlight the most energy-driving building features regarding each end-use. These features are then compared with the collected post-occupancy data. Energy-driving features are progressively replaced with field data for a step-by-step calibration of the energy model. Results of this study provide an analysis of energy performance gap on an existing residential case study under deep retrofit actions. It highlights the impact of the different building features on the energy behavior and the performance gap in this context, such as temperature setpoints, indoor occupancy, the building envelopeproperties but also domestic hot water usage or heat gains from electric appliances. The benefits of inputting field data from an extensive instrumentation campaign instead of standardized scenarios are also described. Finally, the exhaustive instrumentation solution provides useful insights on the needs, advantages, and shortcomings of the implemented sensor network for its replicability on a larger scale and for different use cases.

Keywords: calibration, building energy modeling, performance gap, sensor network

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Authors: Matteo Nicoli, Gianvito Colucci, Valeria Di Cosmo, Daniele Lerede, Laura Savoldi

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The transport sector is currently responsible for approximately 1/3 of greenhouse gas emissions in Europe. In the wider context of achieving carbon neutrality of the global energy system, different alternatives are available to decarbonizethe transport sector. In particular, while electricity is already the most consumed energy commodity in rail transport, battery electric vehicles are one of the zero-emissions options on the market for road transportation. On the other hand, hydrogen-based fuel cell vehicles are available for road and non-road vehicles. The European Commission is strongly pushing toward the integration of hydrogen in the energy systems of European countries and its widespread adoption as an energy vector to achieve the Green Deal targets. Furthermore, the Italian government is defining hydrogen-related objectives with the publication of a dedicated Hydrogen Strategy. The adoption of energy system optimization models to study the possible penetration of alternative zero-emitting transport technologies gives the opportunity to perform an overall analysis of the effects that the development of innovative technologies has on the entire energy system and on the supply-side, devoted to the production of energy carriers such as hydrogen and electricity. Using an open-source modeling framework such as TEMOA, this work aims to compare the role of hydrogen and electric vehicles in the decarbonization of the transport sector. The analysis investigates the advantages and disadvantages of adopting the two options, from the economic point of view (costs associated with the two options) and the environmental one (looking at the emissions reduction perspectives). Moreover, an analysis on the profitability of the investments in hydrogen and electric vehicles will be performed. The study investigates the evolution of energy consumption and greenhouse gas emissions in different transportation modes (road, rail, navigation, and aviation) by detailed analysis of the full range of vehicles included in the techno-economic database used in the TEMOA model instance adopted for this work. The transparency of the analysis is guaranteed by the accessibility of the TEMOA models, based on an open-access source code and databases.

Keywords: battery electric vehicles, decarbonization, energy system optimization models, fuel cell vehicles, hydrogen, open-source modeling, TEMOA, transport

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Authors: Aldin Justin Sundararaj, Austin Lord Tennyson, Divya Jose, A. N. Subash

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Blast waves are generated due to the explosions of high energy materials. An explosion yielding a blast wave has the potential to cause severe damage to buildings and its personnel. In order to understand the physics of effects of blast pressure on buildings, studies in the shock tube on generic configurations are carried out at various pressures on discrete models. The strength of shock wave is systematically varied by using different driver gases and diaphragm thickness. The basic material of the diaphragm is Aluminum. To simulate the effect of shock waves on discrete models a shock tube was used. Generic models selected for this study are suitably scaled cylinder, cone and cubical blocks. The experiments were carried out with 2mm diaphragm with burst pressure ranging from 28 to 31 bar. Numerical analysis was carried out over these discrete models. A 3D model of shock-tube with different discrete models inside the tube was used for CFD computation. It was found that cone has dissipated most of the shock pressure compared to cylinder and cubical block. The robustness and the accuracy of the numerical model were validation with the analytical and experimental data.

Keywords: shock wave, blast wave, discrete models, shock tube

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Authors: A. Beril Tugrul

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Demand on production and thereby the global need of energy is growing continuously. Each country has different trends on energy demand and supply according to their geopolitical and geographical locations, underground reserves, weather conditions and level of industrialization. Conventional energy resources such as oil, gas and coal –in other words fossil resources- remain dominant on primary energy supply in spite of causing of environmental problems. Energy supply and demand securities are essential within the energy importing and exporting countries. This concept affected all sectors, but especially impressed on political aspects of the countries and also global economic views.

Keywords: energy policy, energy economics, energy strategy, global trends, petro-dollar recycling

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Authors: Klathea Sevilla, Marjorie Remolador, Bryan Baltazar, Imee Saladaga, Loureal Camille Inocencio, Ma. Rosario Concepcion Ang

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The underutilization of biomass resources in the Philippines, combined with its growing population and the rise in fossil fuel prices confirms demand for alternative energy sources. The goal of this paper is to provide a comparison of MODIS-based and Landsat-based agricultural land cover maps when used in the estimation of rice hull’s available energy potential. Biomass resource assessment was done using mathematical models and remote sensing techniques employed in a GIS platform.

Keywords: biomass, geographic information system (GIS), remote sensing, renewable energy

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Authors: Azzedine Hamza, Chouaib Chakour, Messaoud Ramdani

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Energy demand prediction plays a crucial role in achieving next-generation power systems for agricultural greenhouses. As a result, high prediction quality is required for efficient smart grid management and therefore low-cost energy consumption. The aim of this paper is to investigate the effectiveness of a hybrid data-driven model in day-ahead energy demand prediction. The proposed model consists of Discrete Wavelet Transform (DWT), and Adaptive Neuro-Fuzzy Inference System (ANFIS). The DWT is employed to decompose the original signal in a set of subseries and then an ANFIS is used to generate the forecast for each subseries. The proposed hybrid method (DWT-ANFIS) was evaluated using a greenhouse energy demand data for a week and compared with ANFIS. The performances of the different models were evaluated by comparing the corresponding values of Mean Absolute Percentage Error (MAPE). It was demonstrated that discret wavelet transform can improve agricultural greenhouse energy demand modeling.

Keywords: wavelet transform, ANFIS, energy consumption prediction, greenhouse

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Authors: John Sweeney, Martin Leeming, Raj Thaker, Cristina L. Tuinea-Bobe

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Shrink wrapping is widely used as a method for secondary packaging to assemble individual items, such as cans or other consumer products, into single packages. This method involves conveying the packages into heated tunnels and so has the disadvantages that it is energy-intensive, and, in the case of aerosol products, potentially hazardous. We are developing an automated packaging system that uses stretch wrapping to address both these problems, by using a mechanical rather than a thermal process. In this study, we present a comparative study of shrink wrapping and stretch wrapping materials to assess the relative capability of candidate stretch wrap polymer film in terms of mechanical response. The stretch wrap materials are of oriented polymer and therefore elastically anisotropic. We are developing material constitutive models that include both anisotropy and nonlinearity. These material models are to be incorporated into computer simulations of the automated stretch wrapping system. We present results showing the validity of these models and the feasibility of applying them in the simulations.

Keywords: constitutive model, polymer, mechanical testing, wrapping system

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Authors: Endang Jati Mat Sahid, Hussain Ali Bekhet

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Robust economic growth, increasing population, and personal consumption are the main drivers for the rapid increase of energy demand in Malaysia. Increasing demand has compounded the issue of national energy security due to over-dependence on fossil fuel, depleting indigenous domestic conventional energy resources which in turns has increased the country’s energy import dependence. In order to improve its energy security, Malaysia has seriously embarked on a renewable energy journey. Many initiatives on renewable energy have been introduced in the past decade. These strategies have resulted in the exploding growth of renewable energy deployment in Malaysia. Therefore, this study investigated the impact of renewable energy deployment on energy security. Secondary data was used to calculate the energy security indicators. The study also compared the results of applying different energy security indicators namely availability, applicability, affordability and acceptability dimension of energy resources. The evaluation shows that Malaysia will experience slight improvement in availability and acceptability dimension of energy security. This study suggests that energy security level could be further enhanced by efficient utilization of energy, reducing carbon content of energy and facilitating low-carbon industries.

Keywords: energy policy, energy security, Malaysia, renewable energy

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Authors: Yungtai Lo

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The current study follows an educational intervention on bottle-weaning to simultaneously evaluate the effect of the bottle-weaning intervention on reducing bottle use, daily milk intake from bottles, and daily energy intake in toddlers aged 11 to 13 months. A shared parameter model and a random effects model are used to jointly model bottle use, daily milk intake from bottles, and daily energy intake. We show in the two joint models that the bottle-weaning intervention promotes bottleweaning, and reduces daily milk intake from bottles in toddlers not off bottles and daily energy intake. We also show that the odds of drinking from a bottle were positively associated with the amount of milk intake from bottles and increased daily milk intake from bottles was associated with increased daily energy intake. The effect of bottle use on daily energy intake is through its effect on increasing daily milk intake from bottles that in turn increases daily energy intake.

Keywords: two-part model, semi-continuous variable, joint model, gamma regression, shared parameter model, random effects model

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Authors: Kostas Kouvaris, Anastasia Eleftheriou, Georgios A. Sarantitis, Apostolos Chondronasios

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To achieve the objective of almost zero carbon energy solutions by 2050, the EU needs to accelerate the development of integrated, highly efficient and environmentally friendly solutions. In this direction, district heating and cooling (DHC) emerges as a viable and more efficient alternative to conventional, decentralized heating and cooling systems, enabling a combination of more efficient renewable and competitive energy supplies. In this paper, we develop a forecasting tool for near real-time local weather and thermal energy demand predictions for an entire DHC network. In this fashion, we are able to extend the functionality and to improve the energy efficiency of the DHC network by predicting and adjusting the heat load that is distributed from the heat generation plant to the connected buildings by the heat pipe network. Two case-studies are considered; one for Vransko, Slovenia and one for Montpellier, France. The data consists of i) local weather data, such as humidity, temperature, and precipitation, ii) weather forecast data, such as the outdoor temperature and iii) DHC operational parameters, such as the mass flow rate, supply and return temperature. The external temperature is found to be the most important energy-related variable for space conditioning, and thus it is used as an external parameter for the energy demand models. For the development of the forecasting tool, we use state-of-the-art deep neural networks and more specifically, recurrent networks with long-short-term memory cells, which are able to capture complex non-linear relations among temporal variables. Firstly, we develop models to forecast outdoor temperatures for the next 24 hours using local weather data for each case-study. Subsequently, we develop models to forecast thermal demand for the same period, taking under consideration past energy demand values as well as the predicted temperature values from the weather forecasting models. The contributions to the scientific and industrial community are three-fold, and the empirical results are highly encouraging. First, we are able to predict future thermal demand levels for the two locations under consideration with minimal errors. Second, we examine the impact of the outdoor temperature on the predictive ability of the models and how the accuracy of the energy demand forecasts decreases with the forecast horizon. Third, we extend the relevant literature with a new dataset of thermal demand and examine the performance and applicability of machine learning techniques to solve real-world problems. Overall, the solution proposed in this paper is in accordance with EU targets, providing an automated smart energy management system, decreasing human errors and reducing excessive energy production.

Keywords: machine learning, LSTMs, district heating and cooling system, thermal demand

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Authors: Torsten Schwan, Rene Unger

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Simulation of thermal and electrical building performance more and more becomes part of an integrative planning process. Increasing requirements on energy efficiency, the integration of volatile renewable energy, smart control and storage management often cause tremendous challenges for building engineers and architects. This mainly affects commercial or non-residential buildings. Their energy consumption characteristics significantly distinguish from residential ones. This work focuses on the many-objective optimization problem indoor air quality and comfort, especially in non-residential buildings. Based on a brief description of intermediate dependencies between different requirements on indoor air treatment it extends existing Modelica-based building physics models with additional system states to adequately represent indoor air conditions. Interfaces to corresponding HVAC (heating, ventilation, and air conditioning) system and control models enable closed-loop analyzes of occupants' requirements and energy efficiency as well as profitableness aspects. A complex application scenario of a nearly-zero-energy school building shows advantages of presented evaluation process for engineers and architects. This way, clear identification of air quality requirements in individual rooms together with realistic model-based description of occupants' behavior helps to optimize HVAC system already in early design stages. Building planning processes can be highly improved and accelerated by increasing integration of advanced simulation methods. Those methods mainly provide suitable answers on engineers' and architects' questions regarding more exuberant and complex variety of suitable energy supply solutions.

Keywords: indoor air quality, dynamic simulation, energy efficient control, non-residential buildings

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Authors: Fazıl Gökgöz, Fahrettin Filiz

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Electricity prices have sophisticated features such as high volatility, nonlinearity and high frequency that make forecasting quite difficult. Electricity price has a volatile and non-random character so that, it is possible to identify the patterns based on the historical data. Intelligent decision-making requires accurate price forecasting for market traders, retailers, and generation companies. So far, many shallow-ANN (artificial neural networks) models have been published in the literature and showed adequate forecasting results. During the last years, neural networks with many hidden layers, which are referred to as DNN (deep neural networks) have been using in the machine learning community. The goal of this study is to investigate electricity price forecasting performance of the shallow-ANN and DNN models for the Turkish day-ahead electricity market. The forecasting accuracy of the models has been evaluated with publicly available data from the Turkish day-ahead electricity market. Both shallow-ANN and DNN approach would give successful result in forecasting problems. Historical load, price and weather temperature data are used as the input variables for the models. The data set includes power consumption measurements gathered between January 2016 and December 2017 with one-hour resolution. In this regard, forecasting studies have been carried out comparatively with shallow-ANN and DNN models for Turkish electricity markets in the related time period. The main contribution of this study is the investigation of different shallow-ANN and DNN models in the field of electricity price forecast. All models are compared regarding their MAE (Mean Absolute Error) and MSE (Mean Square) results. DNN models give better forecasting performance compare to shallow-ANN. Best five MAE results for DNN models are 0.346, 0.372, 0.392, 0,402 and 0.409.

Keywords: deep learning, artificial neural networks, energy price forecasting, turkey

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Authors: Joseph Levodo, Ndimbarafine Young Tobin, E. Messina, P. Edouma

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Energy efficiency can contribute significantly towards increasing clean energy access to modern energy services. Many developing countries have largely focused on expanding energy access by increasing supply. This is due to the fact the links between energy efficiency and clean energy access are often unnoticed. Energy efficiency measures offer the promise of reducing energy use and saving money on electricity bills, as well as reducing negative environmental externalities associated with the production of electricity. This paper seeks to address the economic and effectiveness of reducing energy consumption by integrating energy efficiency as a priority to meet energy access examines the barriers to energy efficient in sub-Saharan African countries. The findings from this study reveal that an appropriate policy can promote the development of more energy-efficient buildings, products and strengthen incentives for consumers, businesses, and industrial customers to pursue cost-effective energy-efficiency measures and to make investments that will provide future energy-efficiency improvements.

Keywords: barriers, Sub-Saharan Africa, cost effective, energy savings, clean energy

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Authors: S. Srinivasan, E. Cretu

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The information flow (e.g. block-diagram or signal flow graph) paradigm for the design and simulation of Microelectromechanical (MEMS)-based systems allows to model MEMS devices using causal transfer functions easily, and interface them with electronic subsystems for fast system-level explorations of design alternatives and optimization. Nevertheless, the physical bi-directional coupling between different energy domains is not easily captured in causal signal flow modeling. Moreover, models of fundamental components acting as building blocks (e.g. gap-varying MEMS capacitor structures) depend not only on the component, but also on the specific excitation mode (e.g. voltage or charge-actuation). In contrast, the energy flow modeling paradigm in terms of generalized across-through variables offers an acausal perspective, separating clearly the physical model from the boundary conditions. This promotes reusability and the use of primitive physical models for assembling MEMS devices from primitive structures, based on the interconnection topology in generalized circuits. The physical modeling capabilities of Simscape have been used in the present work in order to develop a MEMS library containing parameterized fundamental building blocks (area and gap-varying MEMS capacitors, nonlinear springs, displacement stoppers, etc.) for the design, simulation and optimization of MEMS inertial sensors. The models capture both the nonlinear electromechanical interactions and geometrical nonlinearities and can be used for both small and large signal analyses, including the numerical computation of pull-in voltages (stability loss). Simscape behavioral modeling language was used for the implementation of reduced-order macro models, that present the advantage of a seamless interface with Simulink blocks, for creating hybrid information/energy flow system models. Test bench simulations of the library models compare favorably with both analytical results and with more in-depth finite element simulations performed in ANSYS. Separate MEMS-electronic integration tests were done on closed-loop MEMS accelerometers, where Simscape was used for modeling the MEMS device and Simulink for the electronic subsystem.

Keywords: across-through variables, electromechanical coupling, energy flow, information flow, Matlab/Simulink, MEMS, nonlinear, pull-in instability, reduced order macro models, Simscape

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Authors: Naci Büyükkaracığan, Murat Ahmet Ökmen

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Emerging demand for energy in developing countries rapid population growth and industrialization are causing a rapid increase, such as Turkey. Energy is an important and indispensable factor in the industry. At the same time, energy is one of the main indicators that reflect a country's economic and social development potential. There is a linear relationship between the energy consumption and social development, and in parallel this situation, it is seen that energy consumption increase with economic growth and prosperity. In recent year’s, energy sources consumption is increasingly continuing, because of population growth and economy in Turkey. 80% of the energy used in Turkey is supplied from abroad. At the same time, while almost all of the energy obtained from our country is met by hydropower. Alternatively, studies of determining and using potential renewable energy sources such as solar energy have been realized for recent years. In this study, first of all, the situation of energy sources was examined in Turkey. Information of reserve/capacity, production and consumption values of energy sources were emphasized. For this purpose, energy production and consumption, CO2 emission and electricity energy consumption of countries were investigated. Energy consumption and electricity energy consumption per capita were comparatively analyzed.

Keywords: energy potential, alternative energy sources, solar energy, Turkey

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Authors: Y. Tiamaz, N. Souissi

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The purpose of this paper is to present a classification of Lean models which aims to capture all the concepts related to this approach and thus facilitate its implementation. This classification allows the identification of the most relevant models according to several dimensions. From this perspective, we present a review and an analysis of Lean models literature and we propose dimensions for the classification of the current proposals while respecting among others the axes of the Lean approach, the maturity of the models as well as their application domains. This classification allowed us to conclude that researchers essentially consider the Lean approach as a toolbox also they design their models to solve problems related to a specific environment. Since Lean approach is no longer intended only for the automotive sector where it was invented, but to all fields (IT, Hospital, ...), we consider that this approach requires a generic model that is capable of being implemented in all areas.

Keywords: lean approach, lean models, classification, dimensions, holistic view

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Authors: Tasir Khan, Ishfaq Ahmad, Yejuan Wang, Muhammad Salam

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Distribution models of the wind speed data are essential to assess the potential wind speed energy because it decreases the uncertainty to estimate wind energy output. Therefore, before performing a detailed potential energy analysis, the precise distribution model for data relating to wind speed must be found. In this research, material from numerous criteria goodness-of-fits, such as Kolmogorov Simonov, Anderson Darling statistics, Chi-Square, root mean square error (RMSE), AIC and BIC were combined finally to determine the wind speed of the best-fitted distribution. The suggested method collectively makes each criterion. This method was useful in a circumstance to fitting 14 distribution models statistically with the data of wind speed together at four sites in Pakistan. The consequences show that this method provides the best source for selecting the most suitable wind speed statistical distribution. Also, the graphical representation is consistent with the analytical results. This research presents three estimation methods that can be used to calculate the different distributions used to estimate the wind. In the suggested MLM, MOM, and MLE the third-order moment used in the wind energy formula is a key function because it makes an important contribution to the precise estimate of wind energy. In order to prove the presence of the suggested MOM, it was compared with well-known estimation methods, such as the method of linear moment, and maximum likelihood estimate. In the relative analysis, given to several goodness-of-fit, the presentation of the considered techniques is estimated on the actual wind speed evaluated in different time periods. The results obtained show that MOM certainly provides a more precise estimation than other familiar approaches in terms of estimating wind energy based on the fourteen distributions. Therefore, MOM can be used as a better technique for assessing wind energy.

Keywords: wind-speed modeling, goodness of fit, maximum likelihood method, linear moment

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Authors: Kung-Jen Tu, Danny Vernatha

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To assist individual departments within universities in their energy management tasks, this study explores the application of Building Information Modeling in establishing the ‘BIM based Energy Management Support System’ (BIM-EMSS). The BIM-EMSS consists of six components: (1) sensors installed for each occupant and each equipment, (2) electricity sub-meters (constantly logging lighting, HVAC, and socket electricity consumptions of each room), (3) BIM models of all rooms within individual departments’ facilities, (4) data warehouse (for storing occupancy status and logged electricity consumption data), (5) building energy management system that provides energy managers with various energy management functions, and (6) energy simulation tool (such as eQuest) that generates real time 'standard energy consumptions' data against which 'actual energy consumptions' data are compared and energy efficiency evaluated. Through the building energy management system, the energy manager is able to (a) have 3D visualization (BIM model) of each room, in which the occupancy and equipment status detected by the sensors and the electricity consumptions data logged are displayed constantly; (b) perform real time energy consumption analysis to compare the actual and standard energy consumption profiles of a space; (c) obtain energy consumption anomaly detection warnings on certain rooms so that energy management corrective actions can be further taken (data mining technique is employed to analyze the relation between space occupancy pattern with current space equipment setting to indicate an anomaly, such as when appliances turn on without occupancy); and (d) perform historical energy consumption analysis to review monthly and annually energy consumption profiles and compare them against historical energy profiles. The BIM-EMSS was further implemented in a research lab in the Department of Architecture of NTUST in Taiwan and implementation results presented to illustrate how it can be used to assist individual departments within universities in their energy management tasks.

Keywords: database, electricity sub-meters, energy anomaly detection, sensor

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Authors: Hebberly Ahatlan

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The process of digitizing energy systems involves transforming traditional energy infrastructure into interconnected, data-driven systems that enhance efficiency, sustainability, and responsiveness. As smart grids become increasingly integral to the efficient distribution and management of electricity from both fossil and renewable energy sources, the energy industry faces strategic challenges associated with digitalization and interoperability — particularly in the context of modern energy business models, such as virtual power plants (VPPs). The critical challenge in modern smart grids is to seamlessly integrate diverse technologies and systems, including virtualization, grid computing and service-oriented architecture (SOA), across the entire energy ecosystem. Achieving this requires addressing issues like semantic interoperability, IT/OT convergence, and digital asset scalability, all while ensuring security and risk management. This paper proposes a four-layer digitalization framework to tackle these challenges, encompassing persistent data protection, trusted key management, secure messaging, and authentication of IoT resources. Data assets generated through this framework enable AI systems to derive insights for improving smart grid operations, security, and revenue generation. Furthermore, this paper also proposes a Trusted Energy Interoperability Alliance as a universal guiding standard in the development of this digitalization framework to support more dynamic and interoperable energy markets.

Keywords: digitalization, IT/OT convergence, semantic interoperability, VPP, energy blockchain

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Authors: Marinko Skare, Malgorzata Porada Rochon

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This paper study the link between energy justice and economic growth. The link between energy justice and growth has not been extensively studied. Here we study the impact and importance of energy justice, as a part of the energy transition process, on economic growth. Our study shows energy justice growth is an important determinant of economic growth and development that should be addressed at the industry and economic levels. We use panel data modeling and causality testing to research the empirical link between energy justice and economic growth. Industry and economy-level policies designed to support energy justice initiatives are beneficial to economic growth. Energy justice is a necessary condition for green growth and sustainability targets.

Keywords: energy justice, economic growth, panel data, energy transition

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Authors: Gonzalo García-Reyes

Abstract:

Conformastat spherically symmetric exact solutions of Einstein's field equations representing matter distributions made of fluid both perfect and anisotropic from given solutions of Poisson's equation of Newtonian gravity are investigated. The approach is used in the construction of new relativistic models of thick spherical shells and three-component models of galaxies (bulge, disk, and dark matter halo), writing, in this case, the metric in cylindrical coordinates. In addition, the circular motion of test particles (rotation curves) along geodesics on the equatorial plane of matter configurations and the stability of the orbits against radial perturbations are studied. The models constructed satisfy all the energy conditions.

Keywords: general relativity, exact solutions, spherical symmetry, galaxy, kinematics and dynamics, dark matter

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Authors: John Okanda Okwaro

Abstract:

Carbon (IV) oxide (CO₂) is emitted majorly from fossil fuel combustion and industrial production. The sources of interest of carbon (IV) oxide in the study area are mining activities, transport systems, and industrial processes. This study is aimed at building models that will help in monitoring the emissions within the study area. Three scenarios were discussed, namely: pessimistic scenario, business-as-usual scenario, and optimistic scenario. The result showed that there was a reduction in carbon dioxide concentration by approximately 50.5 ppm between March 2020 and January 2021 inclusive. This is majorly due to reduced human activities that led to decreased consumption of energy. Also, the CO₂ concentration trend follows the business-as-usual scenario (BAU) path. From the models, the pessimistic, business-as-usual, and optimistic scenarios give CO₂ concentration of about 545.9 ppm, 408.1 ppm, and 360.1 ppm, respectively, on December 31st, 2021. This research helps paint the picture to the policymakers of the relationship between energy sources and CO₂ emissions. Since the reduction in CO₂ emission was due to decreased use of fossil fuel as there was a decrease in economic activities, then if Kenya relies more on green energy than fossil fuel in the post-COVID-19 period, there will be more CO₂ emission reduction. That is, the CO₂ concentration trend is likely to follow the optimistic scenario path, hence a reduction in CO₂ concentration of about 48 ppm by the end of the year 2021. This research recommends investment in solar energy by energy-intensive companies, mine machinery and equipment maintenance, investment in electric vehicles, and doubling tree planting efforts to achieve the 10% cover.

Keywords: forecasting, greenhouse gas, green energy, hierarchical data format

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Authors: Z. Sadeghi, M. R. Omidkhah, M. E. Masoomi

Abstract:

The purpose of this paper is to examine gas transport behavior of mixed matrix membranes (MMMs) combined with porous particles. Main existing models are categorized in two main groups; two-phase (ideal contact) and three-phase (non-ideal contact). A new coefficient, J, was obtained to express equations for estimating effect of the particle porosity in two-phase and three-phase models. Modified models evaluates with existing models and experimental data using Matlab software. Comparison of gas permeability of proposed modified models with existing models in different MMMs shows a better prediction of gas permeability in MMMs.

Keywords: mixed matrix membrane, permeation models, porous particles, porosity

Procedia PDF Downloads 368

Authors: B. E. Weber, N. Vrielink, M. G. Rietbergen

Abstract:

This paper explores the long-term effects of the Energy Box trajectory on households in the private rental sector, specifically households experiencing energy poverty. The concept of energy poverty has been getting increasing attention among policymakers over the past few years. In the Netherlands, as far as we know, there are no national policies on alleviating energy poverty, which negatively impacts energy-poor households. The Energy Box can help households experiencing energy poverty by stimulating them to improve the energy efficiency of their home by changing their energy-saving behavior. Important long-term effects are that respondents indicate that they live in a more environmentally friendly way and that they save money on their energy bills. Households feel engaged with the concept of energy-saving and can see the benefits of changing their energy-saving behavior. Respondents perceived the Energy Box as a means to live more environmentally friendly, instead of it solely being a means to save money on energy bills. The findings show that most respondents signed up for the Energy Box are interested in energy-saving as a lifestyle choice instead of a financial choice, which would likely be the case for households experiencing energy poverty.

Keywords: energy-saving behavior, energy poverty, poverty, private rental sector

Procedia PDF Downloads 91

Authors: Allen A. Espinosa, Arlyne C. Marasigan, Janir T. Datukan

Abstract:

The study explored how students visualize the states and classifications of matter using scientific models. It also identified misconceptions of students in using scientific models. In general, high percentage of students was able to use scientific models correctly and only a little misconception was identified. From the result of the study, a teaching framework was formulated wherein scientific models should be employed in classroom instruction to visualize abstract concepts in chemistry and for better conceptual understanding.

Keywords: visual conception, scientific models, mental models, states of matter, classification of matter

Procedia PDF Downloads 381

Authors: Arvind Dhingra, Tejinder Singh Saggu

Abstract:

As the energy demands rise and the pollution levels grow, it becomes imperative for us to save energy in all the fields in which it is used. The industrial sector is the major commercial energy consuming sector in India, where electrical energy is the most common and widely used type of energy. As the demand and price of energy are increasing day by day, therefore, the subject of energy conservation is a concern for most energy users particularly industry. Judicious use of energy becomes imperative for third world developing country being presence of energy crisis. This paper provides some measure for energy saving that can be commonly recommended for a rolling unit of steel industry. A case of hot rolling unit in JSL Stainless Ltd., Hisar for energy conservation is given. Overall improvement in energy consumption in light of the stated recommendation is illustrated along with the proposed utilization of the techniques and their applications. Energy conservation in conventional motor with replacement or use of star delta star converter, reduction in cable losses, replacement of filament of LED lamps, replacement of conventional transformer with cast resin dry type transformer and provision of energy management system for energy conservation and per unit production cost reduction are elaborated in this paper.

Keywords: energy audit, energy conservation, energy efficient motors

Procedia PDF Downloads 508