Search

In urban building management, accurate prediction of building energy consumption is significant in realizing energy conservation and improving energy efficiency. Due to the complexity and variability of energy consumption data, existing prediction models face the challenge of difficult parameter selection, which directly affects their accuracy and application. To solve this problem, this study proposes an improved particle swarm algorithm (IPSO) for optimizing the parameters of the least squares support vector machine (LSSVM) and constructing an energy consumption prediction model based on IPSO-LSSVM. The model fully combines the advantages of LSSVM in terms of nonlinear fitting and generalization ability and uses the IPSO algorithm to adjust the parameters precisely. By analyzing the sample data characteristics and validating them on two different types of building energy consumption datasets, the results of the study show that, compared with traditional baseline models such as back-propagation neural networks (BP) and support vector regression (SVR), the model proposed in this study is more accurate and efficient in parameter selection and significantly reduces the prediction error rate. This improved approach not only improves the accuracy of building energy consumption prediction but also enhances the robustness and adaptability of the model, which provides reliable methodological support for the development of more effective energy-saving strategies and optimization of energy use to achieve the goal of energy-saving and consumption reduction and provides a new solution for the future management of building energy consumption. [ABSTRACT FROM AUTHOR]

Aiming at the current power control problems of grid-side electrochemical energy storage power station in multiple scenarios, this paper proposes an optimal power model prediction control (MPC) strategy for electrochemical energy storage power station. This method is based on the power conversion system (PCS) grid-connected voltage and current to establish a power prediction model for energy storage power stations, achieving a one-step prediction of the power of the power station. The power prediction error is used as a power regulation feedback quantity to correct the reference power input. Considering the state of charge ( S O C ) constraint of the battery, partition the S O C into different states. Using S O C as the power regulation feedback, the power of the battery compartment can be adjusted according to the range of the battery S O C to prevent S O C from exceeding the limit value, simultaneously calculating the power loss of the energy storage power station to improve the energy efficiency. The objective function is to minimize the power deviation and power loss of the power station. By solving the objective function, the optimal switching voltage vector of the converter output is achieved to achieve optimal power control of the energy storage power station. The simulation results in various application scenarios of the energy storage power station show that the proposed control strategy enables the power of the storage station to quickly and accurately track the demand of grid scheduling, achieving the optimal power control of the electrochemical energy storage power station. [ABSTRACT FROM AUTHOR]

Predictive modeling is important for assessing and reducing energy consumption and CO2 emissions of light-duty vehicles (LDVs). However, LDV emission datasets have not been fully analyzed, and the rich features of the data pose challenges in prediction. This study aims to conduct a comprehensive analysis of the CO2 emission data for LDVs and investigate key prediction model characteristics for the data. Vehicle features in the data are analyzed for their correlations and impact on emissions and fuel consumption. Linear and non-linear models with feature selection are assessed for accuracy and consistency in prediction. The main behaviors of the predictive models are analyzed with respect to vehicle data. The results show that the linear models can achieve good prediction performance comparable to that of nonlinear models and provide superior interpretability and reliability. The non-linear generalized additive models exhibit enhanced accuracy but display varying performance with model and parameter choices. The results verify the strong impact of fuel consumption and powertrain attributes on emissions and their substantial influence on the prediction models. The paper uncovers crucial relationships between vehicle features and CO2 emissions from LDVs. These findings provide insights for model and parameter selections for effective and reliable prediction of vehicle emissions and fuel consumption. [ABSTRACT FROM AUTHOR]

With the increasing depth of metal mining, thermal damage has become a serious problem that restricts mining. The thermal management system of refrigeration and ventilation is an indispensable technology in the mining of deep metal mines, which plays a key role in improving the thermal and humid environment of mines. Optimizing the performance of refrigeration and ventilation systems to reduce energy consumption has become a focus of researchers' attention. Based on the heat current method, this research establishes the overall heat transfer and flow constraint model of the refrigeration and ventilation system, and proposes an iterative algorithm that combines the refrigerator energy consumption model and the artificial neural network model of heat exchangers. The Lagrange multiplier method is used to optimize the system with the goal of minimizing the total power consumption of the system. The results show that under 9.1 kW cooling load conditions, the total energy consumption of the system reduces by 16.5%, and the COP of the refrigerator increases by 11.6%. The optimization results provide significant guidance for the production and energy consumption reduction of the deep metal mines. [ABSTRACT FROM AUTHOR]

This paper proposes a battery state of charge (SOC)-based energy management strategy using hierarchical distributed model predictive control (HDMPC) for a standalone microgrid on solar-powered long-endurance aircraft. The microgrid was innovatively designed as a two-layer structure in which the first layer consists of a photovoltaic generation and battery storage system named the PV battery module (PBM). The second layer, named the microgrid subsystem (MGSS), consists of several PBMs, each of which supplies power to a specific DC load on the aircraft. The control system is divided into two levels: the grid-level model predictive control (MPC) and the converter-level MPC. The grid-level MPC adopts a distributed model predictive control strategy to obtain the reference power of each module. The converter-level MPC calculates the control variables of converters using a supervisory model predictive control (SMPC) strategy. The new microgrid structure and the proposed control strategy have improved the reliability of the energy system and increased its energy utilization rate. [ABSTRACT FROM AUTHOR]

Fuel-saving-oriented collaborative driving is a highly promising yet challenging endeavor that requires satisfying the driver's operational intentions while surpassing the driver's fuel-saving performance. In light of this challenge, the paper introduces an innovative collaborative driving strategy tailored to the objective of fuel conservation in the context of commercial vehicles. An enhancement to this strategy involves the development of a network prediction model for vehicle speed, leveraging insights from driver style recognition. Employing the predicted speed as a reference, a model-predictive-control-based optimal controller is designed to track the reference while optimizing fuel consumption. Furthermore, a straightforward yet effective collaborative rule is proposed to ensure alignment with the driver's intention. Subsequently, the proposed control scheme is validated through simulation and real-world driving data, revealing that the human–machine cooperative driving controller saves 4% more fuel than human drivers. [ABSTRACT FROM AUTHOR]

A microgrid cluster is composed of multiple interconnected microgrids and operates in the form of cluster, which can realize energy complementation between microgrids and significantly improve their renewable energy consumption capacity and system operation reliability. A microgrid optimal dispatch based on a distributed economic model predictive control algorithm is proposed in this paper. Firstly, the control task of the microgrid power generation system is defined, which is required to meet the load demand while reducing the economic loss of the system and realize dynamic economic optimization. The global objective function is designed based on the control task, and the detailed design method of the distributed economic model predictive controller is given. The control law is obtained by an iterative calculation using the Nash optimal method, which can effectively reduce the amount of data in the communication network. Finally, a microgrid group composed of four microgrids is used as an example for simulation verification. The simulation results show that the distributed economic model predictive control algorithm proposed in this paper has good economic benefits for microgrid dispatching. [ABSTRACT FROM AUTHOR]

This paper presents an equivalent consumption minimization strategy (ECMS) based on model predictive control for series hybrid electric mine trucks (SHE-MTs), the objective of which is to minimize fuel consumption. Two critical works are presented to achieve the goal. Firstly, to gain the real-time speed trajectory on-line, a speed prediction model is established by utilizing a recurrent neural network (RNN). Specifically, a hybrid optimization algorithm based on the genetic algorithm (GA) and the particle swarm optimization algorithm (PSOA) is used to enhance the prediction precision of the speed prediction model. Then, on this basis, an ECMS based on MPC (ECMS-MPC) is proposed. In this process, to improve the real-time and working condition adaptability of the ECMS-MPC, the power-optimal fuel consumption mapping model of the range extender is established, and the equivalent factor (EF) is real-time adjusted by means of the PSOA. Finally, taking a cement mining road as the research object, the proposed strategy is simulated with the collected actual vehicle data. The experimental results indicate that the prediction precision of the proposed speed prediction model is over 98%, realizing on-line speed prediction effectively. Furthermore, compared to the existing real-time EMSs, its fuel-saving rate had an increase of more than 13%. This indicates that the designed ECMS-MPC is able to offer a novel and effective method for the on-line energy management of the SHE-MTs. [ABSTRACT FROM AUTHOR]

Over the past few decades, the electric power industry evolved in response to growing concerns about climate change and the rising price of fossil fuels. The usage of renewable energy sources (RES) rose as a remedy for these problems. The increased penetration of RES in the existing generation system increased the need for an intelligent energy management system (EMS) so that the system can operate in any possible circumstances. Many sectors of society, including the education sector, are working to realize the importance of this sustainable energy system. This paper reviews the process of selecting an efficient control technique for continuous power flow from different RES to meet the load demand requirement using an enhanced model predictive control (MPC)-based EMS framework. This EMS is a software platform to provide fundamental support services and applications to deliver the functionality needed for the effective operation of electrical generation and transmission facilities to ensure adequate security of energy supply at minimum cost. The centralized EMS with technical objectives focusing on power quality and seamless power flow can be achieved through dynamically enhanced MPC. [ABSTRACT FROM AUTHOR]

The engine fuel control system plays a crucial role in engine performance and fuel economy. Fuel control, in traditional engine control systems, is carried out by means of sensor-based control methods, which correct the fuel flow rate through correlations or scheduled parameters in order to reduce the error between a measured parameter and its desired value. In the presence of component degradation, however, the relationship between the engine measurable parameters and performance may lead to an increase in the control error. In this research, linear models for advanced control systems and for direct fuel control in the presence of components degradation are proposed, with the main objective being to directly predict and correct fuel consumption in the presence of degradation instead of adopting measurable parameters. Two techniques were adopted for model linearization: Small Perturbation and System Identification. Results showed that both models are characterized by high accuracy in predicting the output engine variables, with the mean errors between model prediction and data below 1%. The maximum errors, recorded for shaft power, were about 6% for Small Perturbation and lower than 3% for System Identification. A simple correlation between engine performance and components degradation was also demonstrated; in particular, the achieved results allow one to conclude that the Small Perturbation approach is the best candidate for controller development when a prediction of components degradation is included. [ABSTRACT FROM AUTHOR]

Increasing the range of electric vehicles (EVs) is possible with the help of eco-driving techniques, which are algorithms that consider internal and external factors, like performance limits and environmental conditions, such as weather. However, these constraints must include critical variables in energy consumption, such as driver preferences and external vehicle conditions. In this article, a reasonable energy-efficient non-linear model predictive control (NMPC) is built for an electric two-wheeler vehicle, considering the Paris-Brussels route with different driving profiles and driver preferences. Here, NMPC is successfully implemented in a test bed, showing how to obtain the different parameters of the optimization problem and the estimation of the energy for the closed-loop system from a practical point of view. The efficiency of the brushless DC motor (BLCD) is also included for this test bed. In addition, this document shows that the proposal increases the chance of traveling the given route with a distance accuracy of approximately 1.5% while simultaneously boosting the vehicle autonomy by almost 20%. The practical result indicates that the strategy based on an NMPC algorithm can significantly boost the driver's chance of completing the journey. If the vehicle energy is insufficient to succeed in the trip, the algorithm can guide the minimal State of Charge (SOC) required to complete the journey to reduce the driver energy-related uncertainty to a minimum. [ABSTRACT FROM AUTHOR]

The central air conditioning system provides city dwellers with an efficient and comfortable environment. Meanwhile, coinciding with their use, the building electricity load is increased, as central air conditioners consume a lot of electricity. It has become necessary to control central air conditioners for storage and to analyze the energy saving optimization of central air conditioner operation. This study investigates the energy consumption background of central air conditioning systems, and proposes an intelligent load prediction method. With a back propagation (BP) neural network, we use the data collected in the actual project to build the cooling load prediction model for central air conditioning. The network model is also trained using the Levenberg–Marquardt (LM) algorithm, and the established model is trained, tested, and predicted by importing a portion of the sample data, which is filtered by preprocessing. The experimental results show that most of the data errors for training, testing, and prediction are within 10%, indicating that the accuracy achievable by the model can meet the practical requirements, and can be used in real engineering projects. [ABSTRACT FROM AUTHOR]

For the last decade, there has been great interest in studying dynamic control for unmanned aerial vehicles, but drones—although a useful technology in different areas—are prone to several issues, such as instability, the high energy consumption of batteries, and the inaccuracy of tracking targets. Different approaches have been proposed for dealing with nonlinearity issues, which represent the most important features of this system. This paper focuses on the most common control strategy, known as model predictive control (MPC), with its two branches, linear (LMPC) and nonlinear (NLMPC). The aim is to develop a model based on sensors embedded in a Tello quad-rotor used for indoor purposes. The original controller of the Tello quad-rotor is supposed to be the slave, and the designed model predictive controller was created in MATLAB. The design was imported to another embedded system, considered the master. The objective of this model is to track the reference trajectory while maintaining the stability of the system and ensuring low energy consumption. The case study in this paper compares linear and nonlinear model predictive control (MPC). The results show the efficiency of NLMPC, which provides more promising results compared to LMPC. The comparison concentrates on the energy consumption, the tracked trajectory, and the execution time. The main finding of this research is that NLMPC is a good solution to smoothly track the reference trajectory. The controller in this case processes faster, but the rotors consume more energy because of the increased values of control inputs calculated by the nonlinear controller. [ABSTRACT FROM AUTHOR]

In this study, a hierarchical chassis control strategy is designed to enhance vehicle economy and safety for four-wheel independent-drive electric vehicles (FWID-EVs). In the upper-level, a vehicle dynamics model based on multiple agents is proposed, and a distributed model predictive control (DMPC) method is designed to dimensionally solve the problem of tracking the center-of-mass torque of the demanded velocity trajectory and stability parameters. In the bottom-level, a multi-objective torque distribution strategy that weighs safety, dynamics and economy based on multi-agent theory is designed by comprehensively considering the motor efficiency and tire energy loss. Finally, a hardware-in-the-loop (HIL) simulation platform is built to verify the method formulated in this paper. The results show that the method in this paper is effective in tracking the desired trajectory and further enhancing the stability of the vehicle under various conditions. Compared with other algorithms, while guaranteeing safety and dynamics, the energy consumption of the powertrain is reduced by 9.51%. [ABSTRACT FROM AUTHOR]

Traditional building energy consumption prediction methods lack the description of occupant behaviors. The interactions between occupants and equipment have great influence on building energy consumption, which cause a large deviation between the predicted results and the actual situation. To address this problem, a two-part prediction model, consisting of a basic part related to the building area and a variable part related to stochastic occupant behaviors, is proposed in this study. The wavelet decomposition and reconstruction method is firstly used to split the energy consumption. A relationship between the low frequency energy consumption data and the building area is discovered, and an area-based index is used to fit the basic part of the prediction model. With a quantitative description of the occupant–equipment interaction by classifying the equipment into environmentally relevant and environmentally irrelevant equipment, an agent-based model is established in the variable part. According to the validation given by two case office buildings, the prediction error can be controlled to 2.8% and 10.1%, respectively, for the total and the hourly building energy consumption. Compared to the prediction method which does not consider occupant–equipment interactions, the proposed model can improve prediction accuracy by 55.8%. [ABSTRACT FROM AUTHOR]

This paper proposes a model-based predictive control strategy based on mixed-integer linear programming for a photovoltaic power plant with battery energy storage. The control objective is to maximize the revenues from energy delivered from both photovoltaic panels and batteries to the grid in a deregulated electricity market. For each control interval, the proposed algorithm incorporates information on solar radiation, market prices, and the state of charge of the batteries to determine the intervals of energy injection into the grid to maximize the economic benefits. The proposed strategy considers the rate-based variable efficiency in the battery model and time-varying energies prices, thus providing a more general implementation than previous schemes proposed in the literature for the same purpose. Simulations considering the operational procedures of the Spanish market as a case study show that, by integrating the battery efficiency in the model, the proposed control strategy increments the economic benefits in 21% compared to previous results reported in the literature for the same operational conditions. Additionally, the proposed approach reduces the number of charge and discharge cycles, potentially extending the lifespan of batteries. [ABSTRACT FROM AUTHOR]

In order to accurately predict China's future total energy consumption, this article constructs a random forest (RF)–sparrow search algorithm (SSA)–support vector regression machine (SVR)–kernel density estimation (KDE) model to forecast China's future energy consumption in 2022–2030. It is explored whether China can reach the relevant target in 2030. This article begins by using a random forest model to screen for influences to be used as the input set for the model. Then, the sparrow search algorithm is applied to optimize the SVR to overcome the drawback of difficult parameter setting of SVR. Finally, the model SSA-SVR is applied to forecast the future total energy consumption in China. Then, interval forecasting was performed using kernel density estimation, which enhanced the predictive significance of the model. By comparing the prediction results and error values with those of RF-PSO-SVR, RF-SVR and RF-BP, it is demonstrated that the combined model proposed in the paper is more accurate. This will have even better accuracy for future predictions. [ABSTRACT FROM AUTHOR]

In this work, a predictive supervisory controller is presented that optimizes the interaction between a diesel engine and its aftertreatment system (ATS). The fuel consumption is minimized while respecting an upper bound on the emitted tailpipe NOx mass. This is achieved by optimally balancing the fuel consumption, the engine-out NOx emissions, and the ATS heating. The proposed predictive supervisory controller employs a two-layer model predictive control structure and solves the optimal control problem using a direct method. Through experimental validation, the resulting controller was shown to reduce the fuel consumption by 1.1% at equivalent tailpipe NOx emissions for the nonroad transient cycle when compared to the operation with a fixed engine calibration. Further, the controller's robustness to different missions, initial ATS temperatures, NOx limits, and mispredictions was demonstrated. [ABSTRACT FROM AUTHOR]

Optimization of economic aspects of microgrid operation in both grid-connected and islanded mode leads to contradictive definitions of optimality for both modes. There is no general agreement on how to cope with this duality. To address this issue, as well as modern energy market requirements and a better renewable energy utilization necessity in the case of large facilities, a comprehensive control solution utilizing the appropriate model is needed. In response, the authors propose a hybrid microgrid model covering fundamental features and designed to work in conjunction with two switched receding horizon control laws. A relevant controller is chosen according to the current microgrid operation mode and its cost function tailored to specific demands of the islanded or grid-connected operation. Performed research led to a new switched hybrid model predictive control approach focused on microgrid economic optimization. This approach utilizes an appropriate hybrid microgrid model also contributed by the authors. The introduced solution turned out to be effective in overall energy cost reduction in the case of large commercial facilities, regardless of grid-connection and renewable generation scenarios. Furthermore, it also provides satisfactory renewable energy and storage capabilities utilization in changing grid connection conditions. [ABSTRACT FROM AUTHOR]

Energy consumption for heating of single-family residential buildings is a basic item in energy balance and significantly affects their operating costs. Accuracy of heat consumption assessment in existing buildings to a large extent determines the decision on taking actions aimed at heat consumption rationalization, both at the level of a single building and at regional or national level. In the case of energy calculations for the existing buildings, a problem often arises in the form of lack of complete architectural and construction documentation of the analyzed objects. Therefore, there is a need to search for methods that will be suitable for rapid energy analysis in existing buildings. These methods should give satisfactory results in predicting energy consumption when there is limited access to data characterizing the building. Therefore, the aim of this study was to check the usefulness of a model based on thermal characteristics for estimating energy consumption for heating in single-family residential buildings. The research was conducted on a group of 84 buildings, for which the energy characteristics were determined based on the actual energy consumption. In addition, information was collected on variables describing these buildings in terms of construction technology and building geometry, from which the following were extracted for further calculations: cubic capacity, heated area, and year of construction. This made it possible to build a prediction model, which enables the application of a fast, relatively simple procedure of estimating the final energy demand index for heating buildings. The resulting calculations were compared with actual values (calculated from energy bills) and then evaluated according to the standards for evaluating model quality proposed by the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE). In this way, it was possible to determine whether, in the absence of building documents, the indicative method gives good results when estimating the energy demand for heating single-family residential buildings. [ABSTRACT FROM AUTHOR]

Advanced Driver-Assistance Systems (ADASs) are currently gaining particular attention in the automotive field, as enablers for vehicle energy consumption, safety, and comfort enhancement. Compelling evidence is in fact provided by the variety of related studies that are to be found in the literature. Moreover, considering the actual technology readiness, larger opportunities might stem from the combination of ADASs and vehicle connectivity. Nevertheless, the definition of a suitable control system is not often trivial, especially when dealing with multiple-objective problems and dynamics complexity. In this scenario, even though diverse strategies are possible (e.g., Equivalent Consumption Minimization Strategy, Rule-based strategy, etc.), the Model Predictive Control (MPC) turned out to be among the most effective ones in fulfilling the aforementioned tasks. Hence, the proposed study is meant to produce a comprehensive review of MPCs applied to scenarios where ADASs are exploited and aims at providing the guidelines to select the appropriate strategy. More precisely, particular attention is paid to the prediction phase, the objective function formulation and the constraints. Subsequently, the interest is shifted to the combination of ADASs and vehicle connectivity to assess for how such information is handled by the MPC. The main results from the literature are presented and discussed, along with the integration of MPC in the optimal management of higher level connection and automation. Current gaps and challenges are addressed to, so as to possibly provide hints on future developments. [ABSTRACT FROM AUTHOR]

Transportation agencies optimize signals to improve safety, mobility, and the environment. One commonly used objective function to optimize signals is the Performance Index (PI), a linear combination of delays and stops that can be balanced to minimize fuel consumption (FC). The critical component of the PI is the stop penalty "K", which expresses an FC stop equivalency estimated in seconds of pure delay. This study applies vehicular trajectory and FC data collected in the field, for a large fleet of modern vehicles, to compute the K-factor. The tested vehicles were classified into seven homogenous groups by using the k-prototype algorithm. Furthermore, multigene genetic programming (MGGP) is utilized to develop prediction models for the K-factor. The proposed K-factor models are expressed as functions of various parameters that impact its value, including vehicle type, cruising speed, road gradient, driving behavior, idling FC, and the deceleration duration. A parametric analysis is carried out to check the developed models' quality in capturing the individual impact of the included parameters on the K-factor. The developed models showed an excellent performance in estimating the K-factor under multiple conditions. Future research shall evaluate the findings by using field-based K-values in optimizing signals to reduce FC. [ABSTRACT FROM AUTHOR]

There has been increasing interest in reducing carbon footprints globally in recent years. Hence increasing share of green energy and energy efficiency are promoted by governments. Therefore, optimizing energy consumption is becoming more critical for people, companies, industries, and the environment. Predicting energy consumption more precisely means that future energy management planning can be more effective. To date, most research papers have focused on predicting residential building energy consumption; however, a large portion of the energy is consumed by industrial machines. Prediction of energy consumption of large industrial machines in real time is challenging due to concept drift, in which prediction performance deteriorates over time. In this research, a novel data-driven method multi-regime approach (MRA) was developed to better predict the energy consumption for industrial machines. Whereas most papers have focused on finding an excellent prediction model that contradicts the no-free-lunch theorem, this study concentrated on adding potential concept drift points into the prediction process. A real-world dataset was collected from a semi-autonomous grinding (SAG) mill used as a data source, and a deep neural network was utilized as a prediction model for the MRA method. The results proved that the MRA method enables the detection of multi-regimes over time and provides a highly accurate prediction performance, thanks to the dynamic model approach. [ABSTRACT FROM AUTHOR]

The chiller is the major energy consuming HVAC component in a building. Currently, huge chiller data is easy to obtain due to Internet of Things (IoT) technology development. In order to optimize the chiller system, this study presents a data mining technique that utilizes the available chiller data. The data mining techniques used are prediction model, clustering analysis, and association rules mining (ARM) analysis. The dataset was collected every minute for a year from a water-cooled chiller at an institutional building in Taiwan and from meteorological data. The power consumption prediction model was built using deep neural networks with 0.955 of R 2 , 4.470 of MAE, and 6.716 of RMSE. Clustering analysis was performed using the k-means algorithm and ARM analysis was performed using Apriori algorithm. Each cluster identifies those operational parameters that have strong association rules with high performance. The operational parameters from ARM were simulated using the prediction model. The simulation result shows that the ARM operational parameters can successfully save the energy consumption by 22.36 MWh or 18.17% in a year. [ABSTRACT FROM AUTHOR]

Global warming is accelerating due to the increase in greenhouse gas emissions. Accordingly, research on the use of biomass as energy sources, is being actively conducted worldwide to reduce CO2 emissions. Although the production of agro-byproducts is vast, their utilization for energy production has not been fully investigated. This study suggests an optimal torrefaction process condition for agro-byproducts, such as grape branch and perilla, that have moisture content but low calorific values. To determine whether these agro-byproducts can be used for energy sources as substituents of fossil fuels, a mass reduction model was established and validated via experimental results. Thermogravimetric analysis was conducted for different heating rates, and the activation energy and frequency factor were derived through the analysis. The model was developed by changes in rate constants, moisture content, ash content, and lignocellulose content in biomass. To ascertain the optimal torrefaction conditions, fuel characteristic analysis and changes in energy yield of torrefied grape branch and perilla were investigated. The optimal torrefaction conditions for grape branch and perilla were 200 °C for 40 min and 230 °C for 30 min, respectively. The comparison result of the experiment and simulation at the optimum conditions of mass reduction were 1.42%p and 1.51%p, and 15 °C/min and 7.5 °C/min at heating rate, respectively. [ABSTRACT FROM AUTHOR]

To ensure dominance over a multi-domain battlespace, energy and power utilization must be accurately characterized for the dissimilar operational conditions. Using MATLAB/Simulink in combination with multiple neural networks, we created a methodology which was simulated the energy dynamics of a ground vehicle in parallel to running predictive neural network (NN) based predictive algorithms to address two separate research questions: (1) can energy and exergy flow characterization be developed at a future point in time, and (2) can we use the predictive algorithms to extend the energy and exergy flow characterization and derive operational intelligence, used to inform our control based algorithms or provide optimized recommendations to a battlefield commander in real-time. Using our predictive algorithms we confirmed that the future energy and exergy flow characterizations could be generated using the NNs, which was validated through simulation using two separately created datasets, one for training and one for testing. We then used the NNs to implement a model predictive control (MPC) framework to flexibly operate the vehicles thermal coolant loop (TCL), subject to exergy destruction. In this way we could tailor the performance of the vehicle to accommodate a more mission effective solution or a less energy intensive solution. The MPC resulted in a more effective solution when compared to six other simulated conditions, which consumed less exergy than two of the six cases. Our results indicate that we can derive operational intelligence from the predictive algorithms and use it to inform a model predictive control (MPC) framework to reduce wasted energy and exergy destruction subject to the variable operating conditions. [ABSTRACT FROM AUTHOR]

Advances in metering technologies and emerging energy forecast strategies provide opportunities and challenges for predicting both short and long-term building energy usage. Machine learning is an important energy prediction technique, and is significantly gaining research attention. The use of different machine learning techniques based on a rolling-horizon framework can help to reduce the prediction error over time. Due to the significant increases in error beyond short-term energy forecasts, most reported energy forecasts based on statistical and machine learning techniques are within the range of one week. The aim of this study was to investigate how facility managers can improve the accuracy of their building's long-term energy forecasts. This paper presents an extensive study of machine learning and data processing techniques and how they can more accurately predict within different forecast ranges. The Clarendon building of Teesside University was selected as a case study to demonstrate the prediction of overall energy usage with different machine learning techniques such as polynomial regression (PR), support vector regression (SVR) and artificial neural networks (ANNs). This study further examined how preprocessing training data for prediction models can impact the overall accuracy, such as via segmenting the training data by building modes (active and dormant), or by days of the week (weekdays and weekends). The results presented in this paper illustrate a significant reduction in the mean absolute percentage error (MAPE) for segmented building (weekday and weekend) energy usage prediction when compared to unsegmented monthly predictions. A reduction in MAPE of 5.27%, 11.45%, and 12.03% was achieved with PR, SVR and ANN, respectively. [ABSTRACT FROM AUTHOR]

This paper addresses the leader tracking problem for a platoon of heterogeneous autonomous connected fully electric vehicles where the selection of the inter-vehicle distance between adjacent vehicles plays a crucial role in energy consumption reduction. In this framework, we focused on the design of a cooperative driving control strategy able to let electric vehicles move as a convoy while keeping a variable energy-oriented inter-vehicle distance between adjacent vehicles which, depending on the driving situation, was reduced as much as possible to guarantee air-drag reduction, energy saving and collision avoidance. To this aim, by exploiting a distance-dependent air drag coefficient formulation, we propose a novel distributed nonlinear model predictive control (DNMPC) where the cost function was designed to ensure leader tracking performances, as well as to optimise the inter-vehicle distance with the aim of reducing energy consumption. Extensive simulation analyses, involving a comparative analysis with respect to the classical constant time headway (CTH) spacing policy, were performed to confirm the capability of the DNMPC in guaranteeing energy saving. [ABSTRACT FROM AUTHOR]

The sustainable agriculture cultivation in greenhouses is constantly evolving thanks to new technologies and methodologies able to improve the crop yield and to solve the common concerns which occur in protected environments. In this paper, an MPC-based control system has been realized in order to control the indoor air temperature in a high efficiency greenhouse. The main objective is to determine the optimal control signals related to the water mass flow rate supplied by a heat pump. The MPC model allows a predefined temperature profile to be tracked with an energy saving approach. The MPC has been implemented as a multiobjective optimization model that takes into account the dynamic behavior of the greenhouse in terms of energy and mass balances. The energy supply is provided by a ground coupled heat pump (GCHP) and by the solar radiation while the energy losses related to heat transfers across the glazed envelope. The proposed MPC method was applied in a smart innovative greenhouse located in Italy, and its performances were compared with a traditional reactive control method in terms of deviation of the indoor temperature in respect to the desired one and in terms of electric power consumption. The results demonstrated that, for a time horizon of 20 h, in a greenhouse with dimensions 15.3 and 9.9 m and an average height of 4.5 m, the proposed MPC approach saved about 30% in electric power compared with a relay control, guaranteeing a consistent and reliable temperature profile in respect to the predefined tracked one. [ABSTRACT FROM AUTHOR]

Wave power is an eco-friendly power generation method. Owing to the highly volatile nature of wave energy, the application of prediction techniques for power generation, failure diagnosis, and operational efficiency plays a key role in the successful operation of wave power plants (WPPs). To this end, we propose the following approaches: (i) deriving the correlation between highly volatile data such as wave height data and sensor data in an oscillating water column (OWC) chamber; (ii) development of an optimal training model capable of accurate prediction of the state of the wave energy converter (WEC) based on the collected sensor data. In this study, we developed a big data analysis system that can utilize the machine learning framework in KNIME (an open analysis platform), and to enable smart operation, we designed a training model using a digital twin of an OWC–WEC that is currently in operation. Using various machine learning models, the pressure of the OWC chamber was predicted, and the results obtained were tested and evaluated to confirm its validity. Furthermore, the prediction performance was comparatively analyzed, demonstrating the excellent performance of the proposed CNN-LSTM-based prediction model. [ABSTRACT FROM AUTHOR]

This study investigates the impacts of vehicular, operational, topological, and external parameters on the energy consumption (EC) of battery-electric buses (BEBs) in transit operation. Furthermore, the study develops a data-driven prediction model for BEB energy consumption in transit operation that considers these four parameters. A Simulink energy model is developed to estimate the EC rates and validated using the Altoona's test real-world data. A full-factorial experiment is used to generate 907,199 scenarios for BEB operation informed by 120 real-world drive cycles. A multivariate multiple regression model was developed to predict BEB's EC. The regression model explained more than 96% of the variation in the EC of the BEBs. The results show the significant impacts of road grade, the initial state of charge, road condition, passenger loading, driver aggressiveness, average speed, HVAC, and stop density on BEB's energy consumption, each with a different magnitude. The study concluded that the optimal transit profile for BEB operation is associated with rolling grade and relatively lower stop density (one to two stops/km). [ABSTRACT FROM AUTHOR]

Energy efficiency in the building industry is related to the amount of energy that can be saved through thermal improvement. Therefore, it is important to determine the energy saving potential of the buildings to be thermally upgraded in order to check whether the set targets for the amount of energy saved will be reached after the implementation of corrective measures. In real residential buildings, when starting to make energy calculations, one can often encounter the problem of incomplete architectural documentation and inaccurate data characterizing the object in terms of thermal (thermal resistance of partitions) and usable (number of inhabitants). Therefore, there is a need to search for methods that will be suitable for quick technical analysis of measures taken to improve energy efficiency in existing buildings. The aim of this work was to test the usefulness of the type Takagi-Sugeno fuzzy models of inference model for predicting the energy efficiency of actual residential buildings that have undergone thermal improvement. For the group of 109 buildings a specific set of important variables characterizing the examined objects was identified. The quality of the prediction models developed for various combinations of input variables has been evaluated using, among other things, statistical calibration standards developed by the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE). The obtained results were compared with other prediction models (based on the same input data sets) using artificial neural networks and rough sets theory. [ABSTRACT FROM AUTHOR]

Next-generation cellular networks are large-scale systems composed of numerous base stations interacting with many diverse users. One of the main challenges with these networks is their high energy consumption due to the expected number of connected devices. We handle this issue with a coalitional Model Predictive Control (MPC) technique for the case of next-generation cellular networks powered by renewable energy sources. The proposed coalitional MPC approach is applied to two simulated scenarios and compared with other control methods: the traditional best-signal level mechanism, a heuristic algorithm, and decentralized and centralized MPC schemes. The success of the coalitional strategy is considered from an energy efficiency perspective, which means reducing on-grid consumption and improving network performance (e.g., number of users served and transmission rates). [ABSTRACT FROM AUTHOR]

Due to environmental concerns, the energy-saving train regulation is necessary for urban metro transportation, which can improve the service quality and energy efficiency of metro lines. In contrast to most of the existing research of train regulation based on centralized control, this paper studies the energy-saving train regulation problem by utilizing distributed model predictive control (DMPC), which is motivated by the breakthrough of vehicle-based train control (VBTC) technology and the pressing real-time control demand. Firstly, we establish a distributed control framework for train regulation process assuming each train is self-organized and capable to communicate with its preceding train. Then we propose a DMPC algorithm for solving the energy-saving train regulation problem, where each train determines its control input by minimizing a constrained local cost function mainly composed of schedule deviation, headway deviation, and energy consumption. Finally, simulations on train regulation for the Beijing Yizhuang metro line are carried out to demonstrate the effectiveness of the proposed DMPC algorithm, and the results reveal that the proposed algorithm exhibits significantly improved real-time performance without deteriorating the service quality or energy efficiency compared with the centralized MPC method. [ABSTRACT FROM AUTHOR]

With the rapid development of urban rail transit systems and the consequent sharp increase of energy consumption, the energy-saving train operation problem has been attracting much attention. Extensive studies have been devoted to optimal control of a single metro train in an inter-station run to minimize the energy consumption. However, most of the existing work focuses on offline optimization of the energy-saving driving strategy, which still needs to be tracked in real train operation. In order to attain better performance in the presence of disturbances, this paper studies the online optimization problem of the energy-saving driving strategy for a single metro train, by employing the model predictive control (MPC) approach. Firstly, a switched-mode dynamical system model is introduced to describe the dynamics of a metro train. Based on this model, an MPC-based online optimization problem is formulated for obtaining the optimal mode switching times with minimal energy consumption for a single train in an inter-station run. Then we propose an algorithm to solve the constrained optimization problem at each time step by utilizing the exterior point penalty function method. The proposed online optimal train control algorithm which determines the mode switching times can not only improve the computational efficiency but also enhances the robustness to disturbances in real scenarios. Finally, the effectiveness and advantages of this online optimal train control algorithm are illustrated through case studies of a single train in an inter-station run. [ABSTRACT FROM AUTHOR]

Wind energy has been widely used in renewable energy systems. A probabilistic prediction that can provide uncertainty information is the key to solving this problem. In this paper, a short-term direct probabilistic prediction model of wind power is proposed. First, the initial data set is preprocessed by a box plot and gray correlation analysis. Then, a generalized method is proposed to calculate the natural gradient and the improved natural gradient boosting (NGBoost) model is proposed based on this method. Finally, blending fusion is used in order to enhance the learning effect of improved NGBoost. The model is validated with the help of measured data from Dalian Tuoshan wind farm in China. The results show that under the specified confidence, compared with the single NGBoost metamodel and other short-term direct probability prediction models, the model proposed in this paper can reduce the forecast area coverage probability while ensuring a higher average width of prediction intervals, and can be used to build new efficient and intelligent energy power systems. [ABSTRACT FROM AUTHOR]

This article presents the analysis of the influence of household appliances on the quality of the energy consumed by the end-user. The results of the research, then, concern the final consumer (the lowest level of the power grid). The research was conducted on 120 combinations of electrical appliances connected into a grid. Each combination consisted of three devices working simultaneously in a micro-grid. The obtained and statistically analyzed data proved that there are several types of appliances that have a great influence on the power quality (PQ) parameters changes. The results of the conducted experiments indicate the devices which influenced significantly the total harmonic distortion of voltage (THDV), the voltage frequency (FREQ) and the voltage fluctuation (V). Specific features of particular devices were examined in terms of their significance for the power quality deviation. This showed the most important features which should be considered while working out the prediction model. The future of smart grids resides in data analysis, predictive models and real-time optimization. One of the key characteristics is the reducing energy consumption generated by renewable energy sources. This phenomenon, namely looking for problems connected with sustainable power quality and their appropriate solution, is described in this article. We performed an extended analysis of the smart home appliances influence of individual quantities on a real model. Furthermore, we explored devices with a high impact on chosen power quality indicators. In the end, we discuss their specific behavior and relevance to the above-described phenomenon to improve the predictive model utility. [ABSTRACT FROM AUTHOR]

Energy baseline is an important method for measuring the energy-saving benefits of chiller system, and the benefits can be calculated by comparing prediction models and actual results. Currently, machine learning is often adopted as a prediction model for energy baselines. Common models include regression, ensemble learning, and deep learning models. In this study, we first reviewed several machine learning algorithms, which were used to establish prediction models. Then, the concept of clustering to preprocess chiller data was adopted. Data mining, K-means clustering, and gap statistic were used to successfully identify the critical variables to cluster chiller modes. Applying these key variables effectively enhanced the quality of the chiller data, and combining the clustering results and the machine learning model effectively improved the prediction accuracy of the model and the reliability of the energy baselines. [ABSTRACT FROM AUTHOR]

Modern agriculture represents an economic sector that can mainly benefit from technology innovation according to the principles suggested by Industry 4.0 for smart farming systems. Greenhouse industry is significantly becoming more and more technological and automatized to improve the quality and efficiency of crop production. Smart greenhouses are equipped with forefront IoT- and ICT-based monitoring and control systems. New remote sensors, devices, networking communication, and control strategies can make available real-time information about crop health, soil, temperature, humidity, and other indoor parameters. Energy efficiency plays a key role in this context, as a fundamental path towards sustainability of the production. This paper is a review of the precision and sustainable agriculture approaches focusing on the current advance technological solution to monitor, track, and control greenhouse systems to enhance production in a more sustainable way. Thus, we compared and analyzed traditional versus model predictive control methods with the aim to enhance indoor microclimate condition management under an energy-saving approach. We also reviewed applications of sustainable approaches to reach nearly zero energy consumption, while achieving nearly zero water and pesticide use. [ABSTRACT FROM AUTHOR]

The global consumption of dairy produce is forecasted to increase by 19% per person by 2050. However, milk production is an intense energy consuming process. Coupled with concerns related to global greenhouse gas emissions from agriculture, increasing the production of milk must be met with the sustainable use of energy resources, to ensure the future monetary and environmental sustainability of the dairy industry. This body of work focused on summarizing and reviewing dairy energy research from the monitoring, prediction modelling and analyses point of view. Total primary energy consumption values in literature ranged from 2.7 MJ kg−1 Energy Corrected Milk on organic dairy farming systems to 4.2 MJ kg−1 Energy Corrected Milk on conventional dairy farming systems. Variances in total primary energy requirements were further assessed according to whether confinement or pasture-based systems were employed. Overall, a 35% energy reduction was seen across literature due to employing a pasture-based dairy system. Compared to standard regression methods, increased prediction accuracy has been demonstrated in energy literature due to employing various machine-learning algorithms. Dairy energy prediction models have been frequently utilized throughout literature to conduct dairy energy analyses, for estimating the impact of changes to infrastructural equipment and managerial practices. [ABSTRACT FROM AUTHOR]

Advanced thermal control technologies have been continuously developed to complement conventional models and algorithms to improve their performance regarding control accuracy and energy efficiency. This study analyses the strengths and weaknesses of simultaneous controls for the amount of air and its temperature by use of on-demand and predictive control strategies responding to two different outdoor conditions. The framework performs the comparative analyses of an on-demand model, which reacts immediately to indoor conditions, and a predictive model, which provides reference signals derived from data learned. Two models are combined to make a comparison of how much more efficient the combined model operates than each model when abnormal situations occur. As a result, when the two models are combined, its efficiency improves from 20.0% to 33.6% for indoor thermal dissatisfaction and from 13.0% to 44.5% for energy use, respectively. This result implies that in addition to creating new algorithms to cope with any abnormal situation, combining existing models can also be a resource-saving approach. [ABSTRACT FROM AUTHOR]

This paper proposed a multi-objective guaranteed feasible connected and autonomous vehicle (CAV) platoon control method for signalized isolated intersections with priorities. Specifically, we prioritized the intersection throughput and traffic efficiency under a pre-defined signal cycle, based on which we minimized fuel consumption and emissions for CAV platoons. Longitudinal safety was also considered as a necessary condition. To handle the aforementioned targets, we firstly designed a vehicular sub-platoon splitting algorithm based on Farkas lemma to accommodate a maximum number of vehicles for each signal green time phase. Secondly, the CAV optimal trajectories control algorithm was designed as a centralized cooperative model predictive control (MPC). Moreover, the optimal control problem was formulated as discrete linear quadratic control problems with constraints with receding predictive horizons, which can be efficiently solved by quadratic programming after reformulation. For rigor, the proofs of the recursive feasibility and asymptotic stability of our proposed predictive control model were provided. For evaluation, the performance of the control algorithm was compared against a non-cooperative distributed CAV control through simulation. It was found that the proposed method can significantly enhance both traffic efficiency and energy efficiency with ensured safety for CAV platoons at urban signalized intersections. [ABSTRACT FROM AUTHOR]

Nowadays, by huge improvements in industrial control and the necessity of efficient energy consumption for buildings, unified managing systems are established to monitor and control mechanical equipment and energy usage. One of the main portions of the building management system (BMS) is the cooling and heating equipment called heating and ventilation and air-conditioning (HVAC). Based on temperature slow dynamic and presented uncertainty in modeling, a model predictive control (MPC) strategy to track both temperature and humidity is proposed in this study. The main goal of this study is to provide a framework to describe temperature and humidity elements required for dynamic modeling. Following that, by utilizing a predictive approach, a control strategy is obtained, which optimizes the tracking error of two interactional channel and performs the effort control by minimizing the optimization index. Other articles have mostly only had control over the temperature variable, but in our article, we tried to study the equations of temperature and humidity as well as their interference and according to the ASHRAE standard, both temperature and humidity controls must be accurate. The humidity was the novelty in our article. Simulation results proved the effectiveness of the proposed approach compared to the conventional proportional-integral controller. Evidently, the key idea behind the control objective is providing the comfort condition while consuming the least possible energy. [ABSTRACT FROM AUTHOR]

This paper presents a two-level hierarchical energy management system (EMS) for microgrid operation that is based on a robust model predictive control (MPC) strategy. This EMS focuses on minimizing the cost of the energy drawn from the main grid and increasing self-consumption of local renewable energy resources, and brings benefits to the users of the microgrid as well as the distribution network operator (DNO). The higher level of the EMS comprises a robust MPC controller which optimizes energy usage and defines a power reference that is tracked by the lower-level real-time controller. The proposed EMS addresses the uncertainty of the predictions of the generation and end-user consumption profiles with the use of the robust MPC controller, which considers the optimization over a control policy where the uncertainty of the power predictions can be compensated either by the battery or main grid power consumption. Simulation results using data from a real urban community showed that when compared with an equivalent (non-robust) deterministic EMS (i.e., an EMS based on the same MPC formulation, but without the uncertainty handling), the proposed EMS based on robust MPC achieved reduced energy costs and obtained a more uniform grid power consumption, safer battery operation, and reduced peak loads. [ABSTRACT FROM AUTHOR]

Due to the wide application of model predictive control (MPC) in industrial processes, the assessment of MPC performance is essential to ensure product quality and improve energy efficiency. Recently, the slow feature analysis (SFA) algorithm has been successfully applied to assess the performance of MPC. However, the disadvantage of the traditional SFA-based predictable index is that it can only extract one-step predictable information in the monitored variables. In order to better mine the predictable information contained in the monitored variables with large lag, an enhanced method to assess MPC performance based on multi-step SFA (MSSFA) is proposed. Based on the relationship between the slowness of slow features (SFs) and data predictability, an MSSFA model SFA(τ) is built through extending the temporal derivatives of the SFs from one step to multiple steps to extract multi-step predictable information in the monitored variables, which is used to construct a multi-step predictable index. Then, the predictable information in the SFs is further extracted for enhancing the multi-step predictable index to improve its sensitivity to performance changes. The effectiveness of the proposed method has been verified through two process simulation examples. [ABSTRACT FROM AUTHOR]

Understanding the relationships among CO2 emissions, energy consumption, and economic growth helps nations to develop energy sources and formulate energy policies in order to enhance sustainable development. The present research is aimed at developing a novel efficient model for analyzing the relationships amongst the three aforementioned indicators in G20 countries using an adaptive neuro-fuzzy inference system (ANFIS) model in the period from 1962 to 2016. In this regard, the ANFIS model has been used with prediction models using real data to predict CO2 emissions based on two important input indicators, energy consumption and economic growth. This study made use of the fuzzy rules through ANFIS to generalize the relationships of the input and output indicators in order to make a prediction of CO2 emissions. The experimental findings on a real-world dataset of World Development Indicators (WDI) revealed that the proposed model efficiently predicted the CO2 emissions based on energy consumption and economic growth. The direction of the interrelationship is highly important from the economic and energy policy-making perspectives for this international forum, as G20 countries are primarily focused on the governance of the global economy. [ABSTRACT FROM AUTHOR]