Energy utilisation
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The International Communications Satellite Systems Conference (ICSSC) is the oldest and one of the most influential technical conferences in the field. The 37th edition was held from 29 Oct - 1 Nov 2019 in Okinawa, Japan. These proceedings present a broad spectrum of space communications contributions from the conference, with highlights including high speed optical communications and feeder links, advanced digital payloads, broadband satellite communication architectures and applications. Subjects covered include integrated applications and architectures for vessels and IoT; DTN and HTS technologies; new satellite system architectures and components; high speed optical communications and feeder links; advanced digital payloads and components; satellite antenna technologies; propagation and modelling for satellite communications; future technologies for 5G and beyond; flexible HTS systems and advanced digital payloads; satellite networks design challenges and applications; new satellite components and transmitter and modem technologies; NGSO constellations and 5G integration; and NGSO and GSO system issues and interference mitigation techniques. Offering a wide range of expert perspectives on communications satellite systems, these proceedings will be of interest to engineers and researchers in academia and industry working on satellite, digital, and wireless communications and networks, as well as advanced students, policy makers and stakeholders in the field.
This study proposes an approximate dynamic programming (ADP) method for a stochastic home energy management system (HEMS) that aims to minimise the electricity cost and discomfort of a household under uncertainties. The study focuses on a HEMS that optimally schedules heating, ventilation, and air conditioning, water heater, and electric vehicle, while accounting for uncertainties in outside temperature, hot water usage, and non-controllable net load. The authors approach the ADP-based HEMS via an effective combination of Sobol sampling backward induction and a K–D tree nearest neighbour techniques for the value function approximation. A subset of possible states is sampled and used to create an approximation of the value of being in aggregated states. They compare the ADP approach with other prevailing HEMS methods, including dynamic programming (DP) and mixed-integer linear programming (MILP), in a model predictive control framework. Simulation results show that the proposed ADP approach can yield near-optimal appliance schedules under uncertainties when finely discretised. Merits and drawbacks of the proposed ADP method in comparison with DP and MILP are also revealed.
A user-friendly rolling control strategy for a heat pump heating system (HPS) considering building thermal inertia is proposed. A model of a typical HPS is established to reflect the thermal dynamic processes in different HPS parts. A model of the building heat load is also developed considering the actual heat transfer processes related to the building. Based on this, a control strategy framework for the HPS is proposed considering building thermal inertia and temperature comfort level. The optimal temperature set-point of the HPS in each control horizon is determined by the strategy through rolling control. A case study of a single-family residential building on a typical winter day in northern China demonstrates the benefit of the proposed strategy in terms of reducing the operating cost without disturbing the temperature comfort level. The impacts of the penalty factor regarding deviation from the user-expected indoor temperature and of the building envelope are further analysed and compared.
This research undertakes an investigation of global fuel cell vehicle (FCV) deployment, cognizant of optimal economic deployment and stakeholder preferences in a case study of Japan out to the year 2050. The model is mathematically formulated as a large-scale linear optimization problem, aiming to minimize system costs, including generation type, fuel, conversion, and carbon reduction, subject to the constraint of carbon dioxide reduction targets. Results show that between ∼0.8 and 2% of global energy consumption needs can be met by hydrogen by 2050, with city gas and transport emerging as significant use cases. Passenger FCVs and hydrogen buses account for most of the hydrogen-based transportation sector, leading to a global deployment of ∼120 million FCVs by 2050. Hydrogen production is reliant on fossil fuels, and OECD nations are net importers – especially Japan. To underpin hydrogen production from fossil fuels, carbon capture and storage is required in significant quantities when anticipating a large fleet of FCVs. Stakeholder engagement suggests optimism toward FCV deployment while policy issues identified include the necessity for large-scale future energy system investment and rapid technical and economic feasibility progress for renewables and electrolysers to achieve a hydrogen economy which is not reliant on fossil fuels.
The rapid energy consumption around the world has resulted in the shortage of fuel supply and overconsumption of the energy sources, thus worsening the environmental impacts. The overall energy consumption of residential and commercial buildings in the developed countries has reached between 20% and 40%. This figure has exceeded those in the industrial and transportation sectors. Growth in population, increasing demanding for building services and comfort levels and increasing time spent in the buildings indicate that energy demand will continue to increase in the future. For this reason, energy efficiency in buildings is the main goal for energy policies globally.
IoT-driven data centre information system as the hub is crucial for addressing the issues related to building energy management. IoT technology-based monitoring mechanisms for the indoor environment of buildings, as well as energy consumption, bring together all energy consumption equipment for making the construction energy-efficient. The mechanism is apt for various prevailing and new constructions. It is the most appropriate system for transferring data in the indoor environment of buildings and monitoring mechanisms for the energy consumption.As a huge amount of data is produced from IoT devices, intelligent hardware and processing devices are needed to interpret and use the data. This requirement is even greater when we are dealing with real-time computations.
An anti-parallel (AP) diode pair based on a Fermi-level managed barrier (FMB) diode was developed for the sub-harmonic mixing of terahertz waves. A quasi-optical module integrating an AP–FMB diode pair and a trans-impedance amplifier exhibited a very low noise-equivalent-power of 9 × 10−19 W/Hz at an input signal frequency of 304 GHz with a very low local oscillator power of 30 μW.
These days, the trend towards developing electric vehicles technologies, aiming to enlarge the scale of electric vehicles use, is growing rapidly. The catalyst that makes the engineers and researchers motivated to work on this trend is the great concern about the environment, particularly noise and exhaust emissions, in addition to the continuous progress in batteries and fuel cells manufacturing and technology. It is, therefore, essential to understand the principles behind the design of electric vehicles as well as the relevant technological and environmental issues.
The use of renewable energy and the transformation of transport mode are crucial items for achieving an efficient and clean electrical mobility that allow being competitive on the market. In this context the interface between the power system and the Electric Vehicles (EVs) assumes a strategic role. Specifically, wireless energy transmission, based on Inductive Power Transfer (IPT), is an attractive solution for EVs charging. Moreover, the use of electric bicycles or kick scooters as mode of urban transport is continuously growing because they are lightweight, sustainable, easily parking, flexible and efficient transport devices. Owing to its benefits, the wireless power transfer can be considered suitable for those devices. In fact, IPT can also be exploited for Vehicle-To-Grid (V2G), where the wireless power flow can occur from battery to power grid as well. For E-bike applications, bicycle-to-grid or bicycle-to-bicycle energy transfer are viable solutions by means of a Bi-Directional Inductive Power Transfer (BDIPT). In this paper, a 300 W IPT wireless charger prototype for E-bikes is proposed. Modelling, design, simulation and experimental results of this prototype are provided. Open-loop and closed-loop tests have been performed, focusing on system behaviour for different cases of load, distance and misalignment between the coils.
Demand response (DR) programmes offer to customers the opportunity to reduce the power peak and the energy consumption in response to a price signal or financial incentive. Typically, the request to reduce peak demands is made for a specific time period on a specific day, which is referred to as a DR event. To predict a reference energy consumption level in case of different buildings or blocks of buildings within the Technical University of Cluj-Napoca, this study proposes an artificial intelligence enhanced energy profiling method and a more intuitive yet simple method for baseline determination, easy to understand, which allows all the interested parties to estimate the energy and economy savings after a DR event. Once the baseline electric load profile is established, the aim of this study is to calculate some predefined key performance indicators. The two baseline detection methods are compared with each other as a measure of DR event effectiveness. The study has been conducted to clearly demonstrate the economic and environmental benefits of controlling the aggregated load curve in blocks of buildings within several effectively applied DR programmes.
Approximate computing provides a promising way to achieve low power design at the cost of acceptable error. As a core component in a processor, the performance of the multiplier is important. This study presents designs of approximate-truncated Booth multipliers (ATBMs) using proposed approximate modified radix-4 Booth encoders (AMBEs), approximate 4-2 compressors (ACs) and gradually truncated partial products. The accuracy of the ATBMs is adjustable with the so-called approximation factors that indicate the number of AMBEs and ACs used. The normalised mean error distance and the product of the power and delay are used to evaluate the error and the hardware performance of the multipliers. The results show that the proposed ATBMs outperform previous approximate Booth multipliers. Their validity is also shown with case studies of image processing, K-means clustering and handwritten digit recognition.
The increasing demand for high-performance computing has emphasised the invocation of sophisticated multi/many-core computing architecture. Graphical Processing Unit (GPU) is considered to be an essential innovation in this regard as GPU offers a significant amount of parallelism in the execution of complex computing applications. The performance of GPUs in reducing the computational time of such applications is worth mentioning. Although GPUs appear to be a problem-solving solution for complex applications yet high power consumption has been a challenging problem, associated with this many-core computer architecture. Efficient resource management is a emerging and promising solution to this challenge; however, reducing the resources would degrade the system's overall performance. On the other hand, reducing the resources based on the analysis of workload can save significant power without degrading the system's overall performance. Therefore, a smart controller to optimise the resources of general purpose-GPU (GP-GPU) architecture is required. AFBRMC-2, a neuro-fuzzy type-2 based controller, is presented for GP-GPU architecture and, based on a feedback mechanism, keeps analysing the stats of processor and manages resources using dynamic voltage frequency scaling and core gating techniques. The proposed controller achieved up to 55% reduction in power consumption against various benchmarks on the NVIDIA TK1 GPU kit.
In this study, the design routine of a novel phase frequency detector and charge-pump (PFD-CP) is discussed. The main advantage of the proposed circuit is its improved dead zone performance as the circuits of PFD-CP have been merged to reduce the latency of the structure. To justify this, by means of a reconfigurable loop filter, a fast-locking low-power phase-locked loop (PLL) has been implemented which can operate at the range of 100 MHz–1.2 GHz while its power consumption is 2.53 mW at 1.2 GHz operating frequency. The whole PLL is implemented in 0.18 µm complementary metal–oxide–semiconductor technology with a 1.8 V power supply. The post-layout simulation results are provided to show the conformity of theoretical assumptions and circuit-level implementations which depict the locking time of 0.54 µs at 1.2 GHz operating frequency.
This study presents an all-digital power-efficient integrating frequency difference-to-digital converter (iFDDC) and explores its applications in gigahertz (GHz) frequency-locking. The iFDDC utilises a bi-directional gated delay line (BDGDL) to detect and accumulate the frequency difference between two GHz signals and digitises the result with ultra-low power consumption. The built-in integration of the iFDDC ensures that the in-band quantisation noise of the BDGDL and digital controlled oscillator (DCO) is first-order suppressed. The all-digital realisation of the iFDDC makes it fully compatible with technology scaling. The effectiveness of the proposed iFDDC is verified using the simulation results of a 5 GHz frequency-locked loop designed in a Taiwan Semiconductor Manufacturing Company (TSMC) 65 nm 1.2 V complementary metal-oxide-semiconductor (CMOS). The iFDDC consumes only 474 µW, offering the lowest power/frequency efficiency among reported FDDCs. The DCO locks to 5 GHz reference in <10 cycles.
A small-signal model is established for the basic constant voltage (CV) flyback converter firstly. Then the phase margin and the bandwidth, which can reflect the system stability and rapidity, are deduced through transfer function. The parameters affecting the stability of the system can be obtained by the model's derivation to confirm the appropriate external capacitance value. To improve the precision of CV, the compensation of cable is implemented through the module of irrigation current, pulling down the output voltage under any load conditions to the value of it with the maximal load. The prototype for the proposed CV converter has been fabricated in an MXIC 0.8 μm L80A1 process. The minimal static power consumption measured by a test is only 40 mW. In the CV mode, the precision of the output voltage can reach the level of ±1.5%. Therefore, the proposed control chip has a promising application in low power CV AC–DC flyback converter.
This study presents a novel high gain operational amplifier (op-amp) and a comparator using n-type all enhancement amorphous indium-gallium-zinc-oxide (a-IGZO) thin-film transistors (TFTs). The proposed op-amp employs regulated cascode topology in conjunction with capacitive bootstrap load, which enhances the gain to 159.87% (V/V) as compared to op-amp with bootstrapping load. In addition, common mode feedback is introduced in the circuit which improves the common-mode rejection ratio (CMRR) of the amplifier without hampering the output voltage swing. The proposed op-amp offers a voltage gain of 46.2 dB, phase margin of 67°, CMRR of 51.8 dB, unity gain frequency of 215 kHz and power consumption of 0.22 mW. Furthermore, a novel comparator circuit at a clock frequency of 50 kHz is reported. The power consumption of the circuit is 0.248 mW and it can discriminate a minimum voltage of 50 mV. The performance of the proposed circuits is demonstrated using an analytical model of a-IGZO in Cadence environment with a channel length of 20 µm at a supply voltage of 10 V. Further with the help of the circuits reported in this work, many sensing systems of practical importance can be developed, such as smart packaging and bio-medical wearable devices using flexible electronics.
A novel scheme for tunable complementary metal–oxide–semiconductor (CMOS) transconductor robust against process and temperature (PT) variations is presented. The proposed configuration is a voltage controlled circuit based on a double negative channel-metal-oxide-semiconductor (NMOS) transistor differential pairs connected in parallel, which has low power and high linearity. The PT compensation is completed by two identical PT compensation bias voltage generators (PTCBVGs), which can guarantee the designed transconductor high tolerance for PT variations. A complete CMOS transconductor with PTCBVG has been designed and simulated using 0.18 μm technology. The effectiveness of PT compensation technique is proved. The simulation results of post-layout are commensurate with pre-layout. Post-layout simulation results show that when temperature changes from − 40 to 85°C for different process corners (TT, SS, SF, FS and FF), the transconductance varies from 91.8 to 123.6 μS, the temperature coefficient is <1090 ppm/°C, the total harmonic distortion is from − 78 to −72dB at 1 MHz for 0.2 VPP input signal, −3 dB bandwidth changes from 2.5 to 5 GHz, input-referred noise varies from 78.1 to 124.8 nV/sqartHz at 1 MHz and DC power is from 1.5 to 3.2 mW.
To meet rising demands for computing resources, information technology service providers need to select cloud-based services for their vitality and elasticity. Enormous numbers of data centres are designed to meet customer needs. Burning up energy by data centre is very high with the large-scale deployment of cloud data centres. Virtual machine consolidation strategy implementation reduces the data centre energy consumption and guarantees service level agreements. This study proposes a machine learning-based method in cloud computing for the automated use of virtual machines. Machine learning-based virtual machine selection approach integrates the migration control mechanism that enhances selection strategy efficiency. The experiment is performed with various real machine workload circumstances to provide proof and effectiveness of the proposed method. The exploratory outcome shows that the proposed approach streamlines the utilisation of the virtual machine and diminishes the consumption of energy and improves infringement of service level agreements to accomplish better performance.
This chapter introduces the basic concepts on energy efficiency and non -orthogonal multiple access (NOMA) to unlock the potentials of future communication networks. The energy -efficient resource allocation design for NOMA systems is formulated as a non -convex optimization problem. Based on the fractional programming and successive convex approximation (SCA), a generic algorithm is proposed to achieve a suboptimal solution of the formulated problem. Simulation results are provided to verify the convergence of the proposed algorithm and to evaluate the system energy efficiency of the proposed design.
Geometric primitives contained in three-dimensional (3D) point clouds can provide the meaningful and concise abstraction of 3D data, which plays a vital role in improving 3D vision-based intelligent applications. However, how to efficiently and robustly extract multiple geometric primitives from point clouds is still a challenge, especially when multiple instances of multiple classes of geometric primitives are present. In this study, a novel energy minimisation-based algorithm for multi-class multi-instance geometric primitives extraction from the 3D point cloud is proposed. First, an improved sampling strategy is proposed to generate model hypotheses. Then, an improved strategy to establish the neighbourhood is proposed to help construct and optimise an energy function for points labelling. After that, hypotheses and parameters of models are refined. Iterate this process until the energy does not decrease. Finally, models of multi-class multi-instance geometric primitives are simultaneously and robustly extracted from the 3D point cloud. In comparison with the state-of-the-art methods, it can automatically determine the classes and numbers of geometric primitives in the 3D point cloud. Experimental results with synthetic and real data validate the proposed algorithm.