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.

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.

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.

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.

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.

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.

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.

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.

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.

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.