Energy Consumption Load Research Articles

Time-Series Forecasting and Sequence Learning Using Memristor-based Reservoir System

Pushing the frontiers of time-series information processing in the ever-growing domain of edge devices with stringent resources has been impeded by the systems’ ability to process information and learn locally on the device. Local processing and learning of time-series information typically demand intensive computations and massive storage as the process involves retrieving information and tuning hundreds of parameters back in time. In this work, we developed a memristor-based echo state network accelerator that features efficient temporal data processing and in-situ online learning. The proposed design is benchmarked using various datasets involving real-world tasks, such as forecasting the load energy consumption and weather conditions. The experimental results illustrate that the hardware model experiences a marginal degradation in performance as compared to the software counterpart. This is mainly attributed to the limited precision and dynamic range of network parameters when emulated using memristor devices. The proposed system is evaluated for lifespan, robustness, and energy-delay product. It is observed that the system demonstrates reasonable robustness for device failure below 10%, which may occur due to stuck-at faults. Furthermore, 247 × reduction in energy consumption is achieved when compared to a custom CMOS digital design implemented at the same technology node.

Crack characteristic and fractal analysis of SFRC shear wall with CFST columns under repeated low cycle load

In this study, we conducted a comprehensive analysis of the crack characteristics and fractal behavior in steel fiber reinforced concrete shear walls (SFRCSW) reinforced with concrete-filled steel tube columns (CFSTCs) under repeated low-cycle loading conditions after failure. Notably, the incorporation of steel fibers and CFSTCs did not alter the fundamental shear failure mode but effectively constrained crack widths to 2.0 mm and 1.6 mm, respectively, from an initial width of 2.4 mm. The use of steel fibers or CFSTCs led to significant improvements in the surface crack fractal dimension (SCFD) of shear walls, with increase ratios of 9.7 % and 7.88 %, respectively. Conversely, an increase in SCFD was associated with improvements in yield load, peak load, failure load, and cumulative hysteretic energy consumption in SFRCSWs. SCFD is suitable for comparative evaluation of the bearing capacity and energy dissipation capacity of different shear walls, but it is not suitable for comparative evaluation of displacement and ductility. For shear walls, it seems more reasonable to evaluate the damage degree in loading process by displacement - fractal dimension relation, rather than load - fractal dimension relation, with the critical values of fractal dimension are about 1.12 and 1.17.

Virtual energy dispatching method for hybrid microgrid demand response based on Lyapunov optimization

Abstract The hybrid microgrid (HMG) possesses power generation, storage, and distribution capabilities, serving as an integrated foundational structure for advanced localized distribution networks. However, due to significant discrepancies in load demands and energy consumption among different users, the photovoltaic (PV) generation output and end-user load requirements exhibit unpredictability, resulting in relatively low self-sufficiency ratio (SSR) and self-consumption ratio (SCR) of the HMG PV system. To address this issue, this study proposes a virtual energy dispatching method for hybrid microgrid demand response based on Lyapunov optimization. Initially, a bidirectional virtual energy dispatching system model based on energy provider/buyer classification is constructed, optimizing load demands and energy management of both the load side and the main utility grid (MUG) while considering demand response. Subsequently, a Lyapunov optimization-based bidirectional virtual energy dispatching algorithm (LOBVED) is introduced, optimizing queueing systems in stochastic networks based on drift-plus-penalty technique to resolve the dynamic interactions among stability, performance, and energy self-sufficiency in the HMG environment, aiming to maximize the PV SSR and SCR of the HMG while ensuring the reliability of battery energy storage (BES) energy queues. Finally, the effectiveness of the proposed method is validated through MATLAB simulations.

Audit Energi Listrik Gedung Tower 1 Institut Teknologi Sepuluh Nopember Surabaya

Tower 1 at Institut Teknologi Sepuluh Nopember Surabaya is a high-rise building (High Risk Building) in the education and office sectors, experiencing an annual increase in energy consumption. Therefore, regular and thorough energy audits are necessary to save electricity consumption and ensure that usage aligns with the Indonesian Ministry of Energy and Mineral Resources Regulation No. 13 of 2012. An energy audit is a method to calculate the energy consumption level of a building and determine potential energy savings through energy conservation. The Electrical Energy Consumption Intensity (IKE) is used as a parameter to determine whether the building's energy consumption is efficient or wasteful. The initial energy audit of Tower 1 was conducted quantitatively with on-site measurements over one year, covering the building's electrical system and area. The measurements indicated that the IKE of Tower 1 is 50.86 kWh/m²/year. Subsequently, the total installed electrical energy consumption load was calculated, yielding an IKE of 166.15 kWh/m²/year. Based on the initial energy audit results, energy conservation measures were recommended, including upgrading the lighting system with higher lumen lamps and adjusting air conditioning (AC) to the established standards. Implementing these recommendations resulted in an increased IKE of 176.04 kWh/m²/year, which is still considered efficient compared to the ASEAN-USAID standard of 240 kWh/m²/year.

Cutting Characteristics and Reliability Analysis of Conical Picks Containing Prefabricated Grooved Rocks

When a roadheader breaks hard rock in a roadway, the excessive cutting load causes the conical pick to experience abnormal wear, which impacts equipment stability. To improve the reliability of hard rock cutting equipment, the dynamic characteristics of prefabricated grooved rocks during cutting were investigated using a single pick cutting experimental bench in rock crushing experiments, and a conical pick cutting rock breaking finite element model was established. The rock stress state under different cutting depths and spacing conditions was analyzed, and the cutting force and the trend of the change in the specific energy consumption were elucidated. The results show that prefabricated grooves can effectively reduce the cutting load and specific energy consumption, the cutting depth and conical pick–groove spacing are the key factors affecting the cutting characteristics, and a reasonable choice of spacing and depth can help reduce the wear of conical picks and improve the working reliability of roadheaders.

Numerical investigation of the behavior of reinforced concrete beams produced with self-compacting concrete

In this study, 1/2 scaled 16 reinforced concrete beams were compared in terms of concrete type, concrete strength, and stirrup spacing. The variables of this study consist of self-compacting concrete and normal concrete as concrete type, C30 and C60 as concrete strength, and without stirrup, 20 cm, 10 cm and 5 cm spacing as stirrup spacing. All elements were tested with 4-point bending mechanism. The stiffness, ductility, load bearing capacity and energy consumption capacity values of the beams were obtained from the load-displacement curves acquired from the experimental study and the elements were compared over them, and the damages of the beams during the experiments were interpreted. In addition to the experimental study, the numerical analyzes of the beams were conducted with the finite element analysis software. Experimental study results were validated by finite element analysis. When all the results were examined, it was concluded that although the initial stiffness of SCC (self-compacting concrete) was less than NC (normal concrete), the ductility of SCC was higher than that of NC, especially in high strength concretes.

Seismic performance of high-strength steel octagonal double-sided plate joints in wall-type concrete-filled steel tubular columns connected to steel beams

This study explores the use of a novel octagonal double-sided plate joint made from high-strength steel for connecting steel beams to wall-type concrete-filled steel tubular (WCFST) columns, which is a new solution to the problem of traditional side plate extending from the wall and interfering with wall panel installation. Two full-scale test specimens were developed and assessed under cyclic loading. The results illustrate that the joint specimens exhibit considerable energy dissipation capacities. After experimental verification, a reliable finite element model was obtained and used for a parametric analysis of side plate dimensions. The results suggest that the height, thickness, and strength grade of the side plate should be 1.2 times the beam height, 1.0 times the beam flange thickness, and Q460C, respectively. Hence, the well-designed proposed joint can be employed with less steel and is superior to previous joints in terms of load capacity and energy consumption.

Concurrent PV production and consumption load forecasting using CT‐Transformer deep learning to estimate energy system flexibility

AbstractThe integration of renewable energy sources (RESs) into power systems has increased significantly due to technical, economic, and environmental factors, necessitating greater flexibility to manage variable consumption loads and renewable energy generation. Accurate forecasting of solar energy production and consumption load is critical for enhancing power system flexibility. This study introduces a novel deep learning model, a spatial‐temporal hybrid convolutional‐transformer (CT‐Transformer) network with unique features and extended memory capacity. Additionally, a flexibility index (FI) is introduced to evaluate power system flexibility (PSF) based on the forecasting results. The CT‐Transformer forecasts PSF for the next 24 and 168 hours, using the FI to evaluate PSF based on forecasting results. The input data includes meteorological, solar energy production, load demand, and pricing data from France, comprising hourly data from 2015 and 2016 (about 17,520 entries). Data preprocessing involves correcting incomplete and irrelevant segments. The CT‐Transformer's performance is compared to other deep learning techniques, showing superior results with the lowest prediction error (2.5%) and a maximum error of 10.1% (MAE). It also achieved a prediction error of 0.08% for system flexibility, compared to the highest error of 0.96%. This research highlights the CT‐Transformer's potential for accurate RES and load forecasting and PSF evaluation.

Comprehensive model for efficient microgrid operation: Addressing uncertainties and economic considerations

The fabrication of microgrids to harness renewable resources for local load provision has emerged as a promising concept. Efficient energy management and resource utilization within the electricity market have become crucial tasks for microgrid operation. This article presents a comprehensive model that addresses economic and technical considerations, as well as uncertainties associated with load, wind speed, and solar radiation. Specifically, the focus is on optimizing the utilization and energy consumption of controllable loads within a distributed energy system, taking into account consumer preferences and operational constraints. A two-stage planning problem is formulated to minimize microgrid operation costs and consumer payments, while considering load requirements, restrictions, and utility-imposed constraints. To solve this problem, an enhanced algorithm based on the mountain gazelle optimizer with improved local search operators is proposed, significantly enhancing its search capabilities. The retail electricity market is modeled using real-time pricing (RTP) and incentive-based regulation (IBR) tariffs to capture wholesale price fluctuations and discourage simultaneous consumption. Notably, price forecasting becomes essential in scheduling controllable loads, introducing uncertainties that are addressed by a two-point estimate method. Simulation results demonstrate the effectiveness and robustness of the proposed approach, exhibiting low standard deviation and high resilience. The results highlight the potential of the proposed method in achieving cost-effective and reliable operation of microgrids in the face of uncertainties and dynamic market conditions.

Designing a multi-objective energy management system in multiple interconnected water and power microgrids based on the MOPSO algorithm

In this paper, a method of the energy management system (EMS) in multiple microgrids considering the constraints of power flow based on the three-objective optimization model is presented. The studied model specifications, the variable speed pumps in the water network as well and the storage tanks are optimally planned as flexible resources to reduce operating costs and pollution. The proposed method is implemented hierarchically through two primary and secondary control layers. At the primary control level, each microgrid performs local planning for its subscribers and energy generation resources, and their excess or unsupplied power is determined. Then, by sending this information to the central energy management system (CEMS) at the secondary level, it determines the amount of energy exchange, taking into account the limitations of power flow. Energy storage systems (ESS) are also considered to maintain the balance between power generation by renewable energy sources and consumption load. Also, the demand response (DR) program has been used to smooth the load curve and reduce operating costs. Finally, in this article, the multi-objective particle swarm optimization (MOPSO) is used to solve the proposed three-objective problem with three cost functions generation, pollution, and pump operation. Additionally, sensitivity analysis was conducted with uncertainties of 25 % and 50 % in network generation units, exploring their impact on objective functions. The proposed model has been tested on the microgrid of a 33-bus test distribution and 15-node test water system and has been investigated for different cases. The simulation results prove the effectiveness of the integration of water and power network planning in reducing the operating cost and emission of pollution in such a way that the proposed control scheme properly controls the performance of microgrids and the network in interactions with each other and has a high level of robustness, stable behavior under different conditions and high quality of the power supply. In such a way that improvements of 41.1 %, 52.2 %, and 20.4 % in the defined objective functions and the evaluation using DM, SM, and MID indices further confirms the algorithm's enhanced performance in optimizing the specified objective functions by 51 %, 11 %, and 5.22 %, respectively.

An improved MOEA/D for multi-objective flexible job shop scheduling by considering efficiency and cost

To achieve cost reduction and efficiency improvement in the production process of the aerospace industry, this study establishes a mathematical model for the multi-objective flexible job shop scheduling problem (MOFJSP). The model comprehensively considers four optimisation objectives: completion time, tool number, machine load, and machine energy consumption. For solving the MOFJSP, an improved MOEA/D (IMOEA/D) algorithm is proposed. To improve the quality of solutions, the algorithm introduces another global update strategy in addition to the original substitution operation. The population is initialised using four allocation rules, and the neighbourhood is dynamically updated based on the degree of population evolution. The simulation results on a single instance indicate that the simulation time of IMOEA/D has decreased by 10.5% and machine energy consumption has reduced by 9.5%. Moreover, when compared to various classic multi-objective algorithms on 25 benchmark instances, the IMOEA/D algorithm achieved optimal results in 19 instances for inverted generational distance comparisons, 15 instances for hypervolume comparisons and nearly all instances for set coverage comparisons. The results reveal that the proposed IMOEA/D is effective and competitive in solving MOFJSP.

Enhancing non-intrusive load monitoring with weather and calendar feature integration in DAE

The construction of modern power system is key to achieving dual carbon goals, where non-intrusive load monitoring (NILM) plays a vital role in enhancing energy utilization efficiency and energy management. For example, to enable prosumers to better understand the extent of their flexible loads for demand response and peer-to-peer trading, it is essential to be aware of the types and states of loads using the method of NILM. To improve the predictive accuracy and implementation effectiveness of NILM technology, this paper proposes a novel NILM method integrating meteorological and calendar features. It delves deeply into the close connection between external factors such as temperature, precipitation, wind speed, and holidays, and the energy consumption of electrical appliances, constructing additional associative mappings in the training of the Denoising Autoencoder (DAE) model. Test results on the UK-DALE public dataset show that the NILM method proposed in this paper has significant advantages over traditional NILM methods that consider only single-dimensional electrical data features, in terms of load pattern recognition and accuracy in load energy consumption monitoring. This confirms the potential of multi-dimensional feature fusion technology in the application of NILM.

A stochastic approach to determine the energy consumption and synthetic load profiles of different customer types of rural communities

The electricity demand is highly stochastic and unpredictable. This is due to the fact that it is significantly impacted by a number of factors, including the type of load, weather, time of day, seasonality, economic limitations, customers’ way of living, and other randomness factors. An accurate load model is one of the main inputs for the design of an economical and reliable renewable-based rural electrification system for rural communities and demand management systems. This paper presents a generic methodology for determining a rural community's energy consumption load profile, which is used to determine the most cost-effective size of renewable energy sources for rural electrification purposes. To determine the load profile parameters, such as the types of appliances used, their functioning times, functioning windows, and expected minimum and maximum cycle time, a field survey was conducted in four rural electrified Ethiopian villages. Since the survey findings will not fully explain the stochastic nature of the load profile, the load parameters are randomly generated, and a bottom-up approach is used to estimate the rural community's energy usage. In this study, household loads, public institution loads, business loads, and small industrial loads are the main consumer types taken into account. These loads are classified according to their energy usage as weekdays, weekends, and national and religious holidays. A MATLAB program is developed and implemented to obtain the load profiles of different customer groups. The results of this study are assessed in accordance with the multi-tier criterion and verified through the use of the well-known software HOMER Pro- and LoadProGen.

Wind load analysis and cost assessment of a dual-axis stand-alone photovoltaic structure

This study investigated the effect of wind load on the structural stability of a dual axis tracking system developed at the Botswana International University of Science and Technology (BIUST). Through simulations, the study explored wind speed variations, from 8 m s−1 to 28 m s−1, while simultaneously adjusting the azimuth and elevation angles of the structure. Utilizing computational methods and numerical simulations, the study models wind flow interactions with the dual-axis tracker system. This knowledge can be used to improve the system’s design, ensure safety, and optimize energy production and efficiency. The findings of this study contribute to the field of renewable energy, specifically in the context of solar power generation using dual-axis tracker systems. The results offer valuable insights into the wind load behavior, structural response, and energy consumption, facilitating the development of more robust and efficient tracker designs. Furthermore, the study used the levelized cost of electricity (LCOE) to investigate the cost effectiveness of the solar system. The aim was to establish how the energy production of the system, the installation, operation, and maintenance costs compare to other systems using the local electricity tariffs.

Experimental study on the effect of basalt fiber reinforced expanded polystyrene foams on the compressive strength of lightweight concrete

Reducing the structural load and operational energy consumption of buildings is a trend in the development of the construction industry, and lightweight concrete has become a research hotspot because of its dual characteristics of low density and high strength. In this paper, a three-step method is used to prepare lightweight concrete composites. In the first step, basalt fiber powder was adhered to polystyrene foam ball expanded polystyrene spheres (EPSs) by the “ball rolling method” to obtain basalt fiber reinforced epoxy composite balls. In the second step, the epoxy resin composite ball and the configured cement matrix are filled into a prefabricated mold. Finally, the lightweight concrete specimens were overlaid, pressed into shape, and cured for 28 days. By changing the volume ratio, size, and wall thickness of the epoxy composite balls, as well as the length and content of basalt fibers, the lightweight concrete was exhibited a reduction in density and increase in strength. The results showed that the smaller the epoxy composite spheres and the larger the wall thickness are, the higher the compressive strength of the material for a certain volume ratio of epoxy composite spheres will be. The addition of appropriate hollow glass beads can reduce the number of pores in the matrix, but excessive hollow glass beads will reduce the bonding force of the concrete matrix. Basalt fibers not only improve the strength of EPSs but also serve to connect matrix–matrix and matrix–epoxy composite spheres.

Cooling and Heating Load Calculation and Energy Consumption Analysis for Single-family Residences in New York

Cooling and Heating Load Calculation and Energy Consumption Analysis for Single-family Residences in New York

Parametric design of photovoltaic louver integrated shading devices for west facade windows of office buildings in central China

ABSTRACT In order to improve the shading performance and power generation effect of Photovoltaic shading integrated devices (PVSDs) on the west facade of buildings in China, a multi-objective optimization design method is proposed in this paper. Taking a west-facing office building in Zhengzhou, Henan Province, as an example, a parametric model of photovoltaic (PV) louvers for windows on the west facade is established, and parametric design research is carried out on the energy-saving potential of PV louver shading systems. Based on the optimization objectives of power generation of PV louvers, indoor thermal discomfort hours, building cooling load and lighting energy consumption, the performance of the west facade office with PVSD under different design strategies was simulated, and the optimal design strategy was determined according to the preference of decision makers. Compared with the basic type without a non-shading model, the indoor lighting energy consumption of the optimized west facade PVSD is slightly increased. However, overall performance is improved, with a 35% reduction in cooling load, a decrease of 804.59 kWh in total energy consumption, and a shortened summer thermal discomfort time of 404 hours. The power generation of PV louvers can replace 46% of the total energy consumption of the room. The results show that the installation of PVSD on the west facade window is important for alleviating the overheating inside the room and improving the energy-saving and power generation potential of the room.

Adaptive Multi-Criteria-Based Load Balancing Technique for Resource Allocation in Fog-Cloud Environments

Recently, to deliver services directly to the network edge, fog computing, an emerging and developing technology, acts as a layer between the cloud and the IoT worlds. The cloud or fog computing nodes could be selected by IoTs applications to meet their resource needs. Due to the scarce resources of fog devices that are available, as well as the need to meet user demands for low latency and quick reaction times, resource allocation in the fog-cloud environment becomes a difficult problem. In this problem, the load balancing between several fog devices is the most important element in achieving resource efficiency and preventing overload on fog devices. In this paper, a new adaptive resource allocation technique for load balancing in a fog-cloud environment is proposed. The proposed technique ranks each fog device using hybrid multi-criteria decision- making approaches Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order Performance by Similarity to Ideal Solution (FTOPSIS), then selects the most effective fog device based on the resulting ranking set. The simulation results show that the proposed technique outperforms existing techniques in terms of load balancing, response time, resource utilization, and energy consumption. The proposed technique decreases the number of fog nodes by 11%, load balancing variance by 69% and increases resource utilization to 90% which is comparatively higher than the comparable methods.

ETXRE: Energy and delay efficient routing metric for RPL protocol and wireless sensor networks

AbstractInternet of Things is an emerging paradigm based on interconnecting physical and virtual objects with each other and to the Internet. Most connected things fall into the category of constrained devices, with restricted resources (processing power, memory, and energy). These low‐power and lossy networks (LLNs) are known for their instability, high loss rates and low data rates, which makes routing one of the most challenging problems in low‐cost communications. A routing protocol for low‐power and lossy networks (RPL) is a proactive dynamic routing protocol based on IPv6. This protocol defines an objective function (OF) that utilises a set of metrics to select the best possible path to the destination. Minimum rank hysteresis objective function (MRHOF) and objective function zero (OF0) are the most basic OFs, where the first one selects the path to the sink based on the expected transmission count (ETX) metric, and OF0 is based on the hop count (HC). These two metrics prioritise either brute performance (i.e. ETX) or simplicity (i.e. HC). Therefore, using a single metric with an OF can either limit the performance or have an inefficient impact on load management and energy consumption. To overcome these challenges, a routing metric based on MRHOF OF which takes into consideration the link‐based routing metric (i.e. ETX) and node‐based metric (i.e. remaining energy) for route selection is provided. Expected transmission count remaining energy (ETXRE) is evaluated through 36 scenarios with different parameters. Preliminary results show that ETXRE outperforms ETX and RE in terms of end‐to‐end delay by an average of at least 17%, packet delay by 13% and consumes 10% less energy.

Energy analysis of the integration of HRV and direct evaporative cooling for energy efficiency in buildings: a case study in Iraq

Responding to climate change and adapting to global warming requires creative solutions. In Iraq, the most reliable and popular tool to have buildings cooled is airconditioning units (AC). While an evaporative cooler is not enough to achieve thermal comfort in a very hot climate, AC units consume a lot of energy which causes a significant load on the grid in Iraq resulting in increasing the emissions of CO2. This paper investigates the potential energy-saving associated with adopting a new arrangement of heat recovery ventilation (HRV) unit and evaporative cooler to achieve thermal comfort with far less energy. Two sets of efficiencies of both HRV and the evaporative cooler have been considered, and two different envelope performances are also investigated. To properly size the proposed system, an iterative process has been used until the smallest size of the proposed system enough to cool the building is determined. The proposed system has achieved considerable energy savings comprising a reduction of up to 66% in the cooling load energy consumption and a reduction of up to 44% in the overall energy consumption.