Proposing a novel predictive technique using M5Rules-PSO model estimating cooling load in energy-efficient building system

Engineering with Computers - In this study, we employed the most suitable artificial intelligence systems and optimized it with a novel evolutionary algorithm called particle swarm optimization...     

Model reduction and model predictive control of energy-efficient buildings for electrical heating load shifting

In France, buildings account for a significant portion of the electricity consumption (around 68%), due to an important use of electrical heating systems. This results in high peak load in winter and causes tensions on the production-consumption balance. In view of reducing such fluctuations, advanced control systems (including the Model Predictive Control framework) have been developed to shift heating load while maintaining indoor comfort and taking advantage of the building thermal mass. In this paper, a framework for developing optimisation-based control strategies to shift the heating load in buildings is introduced. The balanced truncation method and a time-continuous optimisation method were used to develop a real-time control of the heating power. These two methods are well suited for control problems and yield precise results. The novelty of the approach is to use reduced models derived from advanced building simulation software. A simulation case study demonstrates the controller performance in the synthesis of a predictive model-based optimal energy management strategy for a single-zone test building of the “INCAS” platform built in Le Bourget-du-Lac, France, by the National Solar Energy Institute (INES). The controller exhibits excellent performance, reaching between 6 and 13% cost reduction, and can easily be applied in real-time.     

Cooling load prediction in a district heating and cooling system through simplified robust filter and multilayered neural network

Cooling load is a heat value of cold water used for air conditioning in a district heating and cooling system. Cooling load prediction in a district heating and cooling system is one of the key techniques for smooth and economical operation. In this article, cooling load prediction in such a district heating and cooling system is considered. Unfortunately, since actual cooling load data usually involve measurement noises, outliers, and missing data for several reasons, a prediction method considering the effect of the outliers and missing data is desirable. In this article, a new prediction method using a simplified robust filter to improve a numerical stability problem of a robust filter and a three-layered neural network, is proposed. Applications of the proposed method and some other methods to actual cooling load data in a district heating and cooling system involving outliers and missing data show the usefulness of the proposed method.     

Hybrid shuffled frog leaping algorithm for energy-efficient dynamic consolidation of virtual machines in cloud data centers

Cloud computing aims to provide dynamic leasing of server capabilities as scalable virtualized services to end users. However, data centers hosting cloud applications consume vast amounts of electrical energy, thereby contributing to high operational costs and carbon footprints. Green cloud computing solutions that can not only minimize the operational costs but also reduce the environmental impact are necessary. This study focuses on the Infrastructure as a Service model, where custom virtual machines (VMs) are launched in appropriate servers available in a data center. A complete data center resource management scheme is presented in this paper. The scheme can not only ensure user quality of service (through service level agreements) but can also achieve maximum energy saving and green computing goals. Considering that the data center host is usually tens of thousands in size and that using an exact algorithm to solve the resource allocation problem is difficult, the modified shuffled frog leaping algorithm and improved extremal optimization are employed in this study to solve the dynamic allocation problem of VMs. Experimental results demonstrate that the proposed resource management scheme exhibits excellent performance in green cloud computing.     

A novel energy-efficient ICI cancellation technique for bandwidth improvements through cyclic prefix reuse in an OFDM system

Orthogonal frequency division multiplexing (OFDM), a very promising technique that is leading the evolution in wireless mobile communication to sideline the bandwidth scarcity issue in spectrum allocation, is severely affected by the undesirable effects of the frequency offset error, which generates inter carrier interference (ICI) due to the Doppler shift and local oscillator frequency synchronization errors. There are many ICI cancellation techniques available in the literature, such as self-cancellation (SC), maximum likelihood estimation (MLE), and windowing, but they present a tradeoff between bandwidth redundancy and system complexity. In this study, a new energy-efficient, bandwidth-effective technique is proposed to mitigate ICI through cyclic prefix (CP) reuse at the receiver end. Unlike SC and MLE where the whole OFDM symbol data is transmitted in duplicate to create redundancy at the transmitter end, the proposed technique uses the CP data (which is only 20% of the total symbol bandwidth) to estimate the channel, and it produces similar results with a huge bandwidth saving. The simulation results show that the proposed technique has a significant improvement in error performance, and a comparative analysis demonstrates the substantial improvement in energy efficiency with high bandwidth gain. Therefore, it outperforms the legacy ICI cancellation schemes under consideration.     

Deep learning forecasting for electric demand applications of cooling systems in buildings

This paper presents the application of a deep learning based model for the short-term forecasting of the electric demand in a heating, ventilation, and air conditioning system (HVAC) for the demand response programs of utility companies. The deep learning model is applied through two different approaches comparing their merits. The approaches consist of: (i) a monolithic approach that applies a single large model to forecast the target variables, and (ii) a sequential approach that consists of multiple deep learning models coupled together each targeting a specific energy load within the HVAC system. The model accuracy of both approaches is explored over two case studies applied to the same institutional building; however, the case studies differ in their data source. The first case study uses synthetic data obtained from an eQuest simulation, while the second case study uses measurement data obtained from the building automation system. Results show that the difference in forecasting error of these approaches is negligible; however, the monolithic approach required the least amount of calibration time. Next, this paper explores the application of off-site weather data applied to a building model calibrated with on-site data. The experiments demonstrated that the off-site weather data can be applied with a slight reduction in forecasting performance.     

ANN-based prediction of ferrite fraction in continuous cooling of microalloyed steels

In the present study, an artificial neural networks-based model was developed to predict the ferrite fraction of microalloyed steels during continuous cooling. Fourteen parameters affecting the ferrite fraction were considered as inputs, including the cooling rate, initial austenite grain size, and different chemical compositions. The network was then trained to predict the ferrite fraction amounts as outputs. A multilayer feed-forward back-propagation network was developed and trained using experimental data form literatures. The predicted values are in very good agreement with the measured ones indicating that the developed model is very accurate and has the great ability for predicting the ferrite fraction.     

Improving the performance of load balancing in software-defined networks through load variance-based synchronization

Software-Defined Networking (SDN) is a new network technology that decouples the control plane logic from the data plane and uses a programmable software controller to manage network operation and the state of network components. In an SDN network, a logically centralized controller uses a global network view to conduct management and operation of the network. The centralized control of the SDN network presents a tremendous opportunity for network operators to refactor the control plane and to improve the performance of applications. For the application of load balancing, the logically centralized controller conducts Real-time Least loaded Server selection (RLS) for multiple domains, where new flows pass by for the first time. The function of RLS is to enable the new flows to be forwarded to the least loaded server in the entire network. However, in a large-scale SDN network, the logically centralized controller usually consists of multiple distributed controllers. Existing multiple controller state synchronization schemes are based on Periodic Synchronization (PS), which can cause undesirable situations. For example, frequent synchronizations may result in high synchronization overhead of controllers. State desynchronization among controllers during the interval between two consecutive synchronizations could lead to forwarding loops and black holes. In this paper, we propose a new type of controller state synchronization scheme, Load Variance-based Synchronization (LVS), to improve the load-balancing performance in the multi-controller multi-domain SDN network. Compared with PS-based schemes, LVS-based schemes conduct effective state synchronizations among controllers only when the load of a specific server or domain exceeds a certain threshold, which significantly reduces the synchronization overhead of controllers. The results of simulations show that LVS achieves loop-free forwarding and good load-balancing performance with much less synchronization overhead, as compared with existing schemes.     

一种基于自动回归的改进网格主机负载预测模型

提出了一种基于自动回归（AR）改进的主机负载预测模型,它不仅具有AR模型本身的计算成本小、预测性能稳定的优点,还对AR模型只对未来某个固定时间段的负载预测进行了改进,使之能根据作业的预测执行时间进行主机负载动态预测,同时该模型还充分体现了主机负载变化的自相似性和长期依赖性。实验结果表明,该模型达到了预期的效果。     

Gain Scheduling Control applied to Thermal Barrier in systems of indirect passive heating and cooling of buildings

A critical point for a new Thermal Barrier technique of indirect heating and cooling of buildings under construction is to maintain a constant temperature during the entire year. Such an effect can be obtained with the use of the proposed Gain Scheduling Control (GSC) system which implements a novel Fuzzy-Mixing Gain-Scheduling (FMGS) strategy that is based on the idea of fuzzy mixing (weighting) of local (modal) values of certain (automatically designed) control parameters. An important advantage of this approach is that the same scheme can be used both for scheduling controller gains and for fuzzy mixing the supply fluids in a temperature optimization procedure. Experiments show both excellent performance and effectiveness of the proposed HVAC control system.     

循环冷却水系统成垢过程软件预测

循环冷却水系统中无机垢的形成预测是一个相当复杂的研究课题，涉及到包括热力学、动力学、流体力学、晶体生长和表面化学等方面的知识。到目前为止，人们已经提出许多理论来试图解决此难题。但不足在于这些理论对成垢过程影响因素的讨论很不全面。本文几乎考虑了冷却水中常见的所有成垢阳、阴离子和非成垢离子，详细讨论了水溶液中所存在的各种平衡，并考察了温度、pH值、离子强度等因素对成垢过程的影响。所建立的模型通过实验数据进行了验证。结果显示，模型的计算结果与实验数据大体一致。利用Visual Basic6．0语言开发循环冷却水系统成垢预测专家系统软件。该软件可在windows 9X操作平台下独立运行，具有界面友好、操作方便、运行可靠稳定等优点。     

Transport of drug particles in micropumps through novel actuation

Micropumps with various types of actuations have been used in lab-on-a-chip devices. In order to control the delivery of drug particles both in space and time and avoid clogging, other types of actuation mechanisms may be needed. In this study, a valveless micropump with novel actuation is proposed to transport particles for biomedical and environmental applications. The transport of drug particles through the designed valveless micropump is carried out through computational fluid dynamics combined with discrete particle transport methods. After convergence studies, the effects of actuation frequency, particle size and the resident times on the particle transport are investigated. Interestingly, both the actuation frequency and particle size have a strong effect in terms of resident times and the spatial distribution of the transported particles through the designed micropump. Based on the results obtained, the relationship between actuation frequency, fluid flow, and particle transport through the designed micropump is presented. The computational analysis presented demonstrates that it is possible to optimize the proposed valveless micropump design for specific delivery of drug particles for separation and sorting applications.     

Dropout prediction in e-learning courses through the combination of machine learning techniques

In this paper, a dropout prediction method for e-learning courses, based on three popular machine learning techniques and detailed student data, is proposed. The machine learning techniques used are feed-forward neural networks, support vector machines and probabilistic ensemble simplified fuzzy ARTMAP. Since a single technique may fail to accurately classify some e-learning students, whereas another may succeed, three decision schemes, which combine in different ways the results of the three machine learning techniques, were also tested. The method was examined in terms of overall accuracy, sensitivity and precision and its results were found to be significantly better than those reported in relevant literature.     

A UML-based quantitative framework for early prediction of resource usage and load in distributed real-time systems

This paper presents a quantitative framework for early prediction of resource usage and load in distributed real-time systems (DRTS). The prediction is based on an analysis of UML 2.0 sequence diagrams, augmented with timing information, to extract timed-control flow information. It is aimed at improving the early predictability of a DRTS by offering a systematic approach to predict, at the design phase, system behavior in each time instant during its execution. Since behavioral models such as sequence diagrams are available in early design phases of the software life cycle, the framework enables resource analysis at a stage when design decisions are still easy to change. Though we provide a general framework, we use network traffic as an example resource type to illustrate how the approach is applied. We also indicate how usage and load analysis of other types of resources (e.g., CPU and memory) can be performed in a similar fashion. A case study illustrates the feasibility of the approach.

Modified evolution equations for the precipitation kinetics of complex phases in multi-component systems

Recently, a model has been developed for the evolution of spherical precipitates in multi-component, multi-phase systems based on the assumption of a maximum Gibbs energy dissipation rate. The mean-field type equations determine the rate of change of the radius and the chemical composition of each individual precipitate from a system of linear equations. For precipitates with complex stoichiometric composition, this first approach is not applicable. In this paper, modified evolution equations are presented that allow for consideration of arbitrary compositional constraints in precipitates and thus overcome the limitations of the initial model formulation.     

A mixture of HMM, GA, and Elman network for load prediction in cloud-oriented data centers

The rapid growth of computational power demand from scientific, business, and Web applications has led to the emergence of cloud-oriented data centers. These centers use pay-as-you-go execution environments that scale transparently to the user. Load prediction is a significant cost-optimal resource allocation and energy saving approach for a cloud computing environment. Traditional linear or nonlinear prediction models that forecast future load directly from historical information appear less effective. Load classification before prediction is necessary to improve prediction accuracy. In this paper, a novel approach is proposed to forecast the future load for cloud-oriented data centers. First, a hidden Markov model （HMM） based data clustering method is adopted to classify the cloud load. The Bayesian information criterion and Akaike information criterion are employed to automatically determine the optimal HMM model size and cluster numbers. Trained HMMs are then used to identify the most appropriate cluster that possesses the maximum likelihood for current load. With the data from this cluster, a genetic algorithm optimized Elman network is used to forecast future load. Experimental results show that our algorithm outperforms other approaches reported in previous works.     

Examining different types of collaborative learning in a complex computer-based environment: A cognitive load approach

This study compared the effects of two collaborative learning strategies (Open-ended and Task-based) with an individualized learning strategy on individual learning in a computer-based environment. The experiment sought ecological validity by conducting it under real teaching and homework conditions. Ninety-four students from grade 9 participated in a webpage design task. Cognitive load theory was used to predict that the collaborative approaches would outperform the individualized approach due to reduced cognitive load. This hypothesis was confirmed by performance scores and cognitive load only in the case of the Open-ended collaborative learning condition. Evidence was also found that the Open-ended collaborative learning condition outperformed the Task-based collaborative one. It was concluded that in collaborative learning a more Open-ended task design together with moderate independent sub-task requirements leads to more effective learning.     

Web3D graphics enabled through sensor networks for cost-effective assessment and management of energy efficiency in buildings

The past decade has seen the advent of numerous building energy efficiency visualization and simulation systems; however, most of them rely on theoretical thermal models to suggest building structural design for new constructions and modifications for existing ones. Sustainable methods of construction have made tremendous progress. The example of the German Energy-Plus-House technology uses a combination of (almost) zero-carbon passive heating technologies. A web-enabled X3D visualization and simulation system coupled with a cost-effective set of temperature/humidity sensors can provide valuable insights into building design, materials and construction that can lead to significant energy savings and an improved thermal comfort for residents, resulting in superior building energy efficiency. A cost-effective hardware-software prototype system is proposed in this paper that can provide real-time data driven visualization or offline simulation of 3D thermal maps for residential and/or commercial buildings on the Web.     

A novel wearable system for the online assessment of risk for biomechanical load in repetitive efforts

Work-related Musculo Skeletal Disorders (WMSD) are considered the third main reason for disability and early retirement in the U.S. and are widespread in many occupations, involving both heavy and light biomechanical loads. In Italy, only taking into account the years 2009–2010, it is estimated an exponential increasing in the number of WMSD reports. In particular a 159.7% increment has been reported compared to the 2006 statistics. In this context, it is clear how important correctly diagnosing this kind of pathology is becoming. Traditional methods for WMDS assessment are based on observational techniques, in which experts manually segment, label and evaluate movements with the help of pro-forma sheets. Since these methods are currently based on visual inspection and subjective judgment, they could benefit from objective measurements in terms of both reliability and repeatability. Moreover an automatic tool for ergonomics assessment would vastly reduce the time that an expert needs to carry out the same assessment manually. In this context a novel wearable wireless system capable of assessing the muscular efforts and postures of the human upper limb for WMSDs diagnosis is proposed. The system, being non-obstructive, can be used to monitor workers in ecologic environment while they are carrying on their everyday tasks. A real-time assessment is obtained according to two of the most common indexes for the analysis of risk factors on workplaces: the Rapid Upper Limb Assessment (RULA) and the Strain Index (SI). The system exploits inertial measurement units (IMUs) to reconstruct the upper limb posture, modeled as a 7 degrees of freedom (DoF) kinematic chain. As far as muscular efforts are concerned, surface EMG sensors are used to assess forearm flexor muscles strain. As an example of the proposed system application the results of a first data collection campaign regarding super-market cashiers during everyday real-life operations is reported.Relevance to industry: The presented system has a high potential impact on industry as a timely intervention on the WMSD factors may reduce pathologies and reduce the recovery of expert workers.     

A novel method for modeling of complex wall geometries in smoothed particle hydrodynamics

Smoothed particle hydrodynamics (SPH) has become increasingly important during recent decades. Its meshless nature, inherent representation of convective transport and ability to simulate free surface flows make SPH particularly promising with regard to simulations of industrial mixing devices for high-viscous fluids, which often have complex rotating geometries and partially filled regions (e.g., twin-screw extruders). However, incorporating the required geometries remains a challenge in SPH since the most obvious and most common ways to model solid walls are based on particles (i.e., boundary particles and ghost particles), which leads to complications with arbitrarily-curved wall surfaces. To overcome this problem, we developed a systematic method for determining an adequate interaction between SPH particles and a continuous wall surface based on the underlying SPH equations. We tested our new approach by using the open-source particle simulator “LIGGGHTS” and comparing the velocity profiles to analytical solutions and SPH simulations with boundary particles. Finally, we followed the evolution of a tracer in a twin-cam mixer during the rotation, which was experimentally and numerically studied by several other authors, and ascertained good agreement with our results. This supports the validity of our newly-developed wall interaction method, which constitutes a step forward in SPH simulations of complex geometries.