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The energy landscape for the Low-Voltage (LV) networks is undergoing rapid changes. These changes are driven by the increased penetration of distributed Low Carbon Technologies, both on the generation side (i.e. adoption of micro-renewables) and demand side (i.e. electric vehicle charging). The previously passive ‘fit-and-forget’ approach to LV network management is becoming increasing inefficient to ensure its effective operation. A more agile approach to operation and planning is needed, that includes pro-active prediction and mitigation of risks to local sub-networks (such as risk of voltage deviations out of legal limits). The mass rollout of smart meters (SMs) and advances in metering infrastructure holds the promise for smarter network management. However, many of the proposed methods require full observability, yet the expectation of being able to collect complete, error free data from every smart meter is unrealistic in operational reality. Furthermore, the smart meter (SM) roll-out has encountered significant issues, with the current voluntary nature of installation in the UK and in many other countries resulting in low-likelihood of full SM coverage for all LV networks. Even with a comprehensive SM roll-out privacy restrictions, constrain data availability from meters. To address these issues, this paper proposes the use of a Deep Learning Neural Network architecture to predict the voltage distribution with partial SM coverage on actual network operator LV circuits. The results show that SM measurements from key locations are sufficient for effective prediction of the voltage distribution, even without the use of the high granularity personal power demand data from individual customers.

Low Voltage (LV) networks are increasingly required to cope with challenges they were not designed for, requiring for more active network management (ANM). Crucially, ANM solutions require the availability of accurate network information. In practice, available data on LV networks can be incomplete, a problem often overlooked in prior ANM research. For example, in the U.K. and many developed countries, the lifetime of distribution networks assets spans several decades, with some of the available asset data gathered and maintained over many years. This can often lead to incomplete cable data being available to network operators. To overcome this, we propose a novel machine learning technique to autonomously approximate the missing cable information in LV networks. Our proposed algorithm uses a tree-based search methodology, which approximates the missing cable’s cross section area (XSA) data based on rules engineers used when designing the LV networks. We validate our approach using a large database of real LV networks, where some of the cables’ XSA are treated as unknown and used as ground truth to evaluate the accuracy of the predictions. Moreover, we propose a mechanism that scores the confidence level of the prediction, information which is then presented to the human network planners.

This paper is an opinion piece on the initial assessment of the comprehension of Universiti Malaysia Sabah (UMS) students’ knowledge about UMS EcoCampus programs in the context of campus sustainability, and their suggestions for green initiatives. A total number of more than 350 students were sampled. The students were divided into a working group and worked on abstracts related to campus sustainability. A total number of 35 groups were formed as units of analysis for this study. The assessment process was conducted in four meetings over a one-month period after lecture. For data collection, the 35 working groups were required to come up with an abstract for each group, and the abstracts were being reviewed by several experts in campus sustainability or personnel familiar with the activities organized by UMS EcoCampus Management Centre. The result suggested that some of the students understood the general concepts of sustainability and some were uncertain about it. The uncertainty is mainly due to the context of ‘campus’ sustainability. The sustainability knowledge of students was mostly on recycle and reuse, electronic technology usage in substituting conventional management practices, and “go green” campaigns. Based on the findings, the result indicates that students of UMS would have to be given more exposure on campus sustainability, and it is best to include sustainability element into course syllabus. By then in the near future, the graduates will be “greener” and susceptive to the concept of sustainability.

A waste heat recovery system (WHRS) is used to capture waste heat released from an industrial process, and transform the heat into reusable energy. In practice, it can be difficult to identify the optimal form of a WHRS for a particular installation, since this can depend on various design objectives, which are often mutually exclusive. More so when the number of objectives is large. To address this problem, a multiobjective evolutionary algorithm (MOEA) was used to explore and characterise the trade-off surface within the design space of a particular WHRS. A combination of clustering algorithm and parallel coordinates plots was proposed for use in analysing the results. The trade-off surface is first segmented using a clustering algorithm and parallel coordinates plots are then used to both visualise and understand the resulting set of Pareto-optimal designs. As a case study, a simulation of a WHRS commonly found in the food and drinks process industries was developed, comprising of a desuperheater coupled to a hot water reservoir. The system was parameterised, considering typical objectives, and the MOEA used to build a library of alternative Pareto-optimal designs that can be used by installers. The resulting visualisation are used to better understand the sensitivity of the system’s parameters and their trade-offs, providing another source of information for prospective installations.

The increasing penetration of low carbon technologies and electric vehicles is expected to place a considerable burden to Low Voltage (LV) power distribution networks. The previously passive management of LV networks will no longer be sustainable to cope with this growth. A more proactive approach is required to manage LV networks, usually achieved by using Power System State Estimation (PSSE) tools. The effective use of PSSE tools however, requires accurate reporting of each LV network's topology and configuration of assets (cables) that compose it. This paper presents a method for ongoing accuracy improvement of the LV networks topologies, by automating the process of approximating missing cable information and validating the network architectures. The method proposed was derived based on the assumptions that a specific network infrastructure was selected to meet a certain distributed customer loading requirement. The results obtained agrees with the assumptions, providing also a mechanism that scores the confidence level for the choices made to approximate any missing asset information.

Modern control systems rely heavily on their sensors for reliable operation. Failure of a sensor could destabilize the system, which could have serious consequences to the system's operations. Therefore, there is a need to detect and accommodate such failures, particularly if the system in question is of a safety critical application. In this paper, a sensor failure detection, identification, and accommodation (SFDIA) scheme is presented. This scheme is based on the fully connected cascade (FCC) neural network (NN) architecture. The NN is trained using the neuron by neuron learning algorithm. This NN architecture is chosen because of its efficiency in terms of the number of neurons and the number of inputs required to solve a problem. The SFDIA scheme considers failures in pitch, roll, and yaw rate gyro sensors of an aircraft. A total of 105 experiments were conducted; out of which, only one went undetected. The SFDIA scheme presented here is efficient, compact, and computationally less expensive, in comparison to schemes using, for example, the popular multilayer perceptron NN. These benefits are inherited from the FCC NN architecture.

This paper presents a Gaussian Adaptive Resonance Theory Map (gARTMAP) incorporated to a multi-agent system (MAS) for building intelligent heat management system (IHMS), or the gARTMAP-MAS IHMS. The gARTMAP aims to maintain the desired space temperature defined by the building occupants (thermal comfort management) and improve energy efficiency by intelligently controlling the energy flow and usage in the building (building energy control) with minimal waste. The gARTMAP-MAS IHMS is a three-layer MAS, consisting of an agent in the Source Layer responsible for building energy control, agents in the User Layer for thermal comfort management, and an agent in the Mediator Layer which mediates the communication between the other two layers. Existing MAS typically uses a rule-based approach for its agents, and the rules are generally pre-defined. The incorporation of the gARTMAP to the agents provides additional adaptive capabilities via the use of its online learning mechanism. Simulation results demonstrated that the gARTMAP-MAS IHMS provides better (automated) energy control and thermal comfort management for a building environment in comparison to the existing rule-based MAS approach.

Intelligent energy system refers to the use of intelligent control strategies with the energy system, which can help minimise energy waste and loss, optimise load control, increase and improve energy efficiency and/or maximise the benefits of renewable energy. Renewable energy and intelligent controls are interlinked and correlated with each other. Without intelligent controls, the full benefits of renewable energy technologies may not be achievable, specifically the ground source heat pump (GSHP) technologies. This was observed, especially by the University of Central Lancashire (UCLan) that had incorporated a GSHP to one of its building. The GSHP is under performing due to the inefficient controls implemented with the GSHP. This paper proposes an intelligent multi-agent building management system (MAS BMS) that aims to tackle this issue. Intelligence is provided by an ARTMAP, a type of artificial neural network that provides incremental learning inspired by how human process memory. Simulation results show the proposed intelligent MAS BMS is able to maximise the use of the GSHP effectively by profiling, predicting and coordinating its usage with other energy resources. The proposed method has performed better than the existing control strategies for the GSHP.

A waste heat recovery system (WHRS) on a process with variable output, is an example of an intermittent renewable process. WHRS recycles waste heat into usable energy. As an example, waste heat produced from refrigeration can be used to provide hot water. However, consistent with most intermittent renewable energy systems, the likelihood of waste heat availability at times of demand is low. For this reason, the WHRS may be coupled with a hot water reservoir (HWR) acting as the energy storage system that aims to maintain desired hot water temperature Td (and therefore energy) at time of demand. The coupling of the WHRS and the HWR must be optimised to ensure higher efficiency given the intermittent mismatch of demand and heat availability. Efficiency of an WHRS can be defined as achieving multiple objectives, including to minimise the need for back-up energy to achieve Td, and to minimise waste heat not captured (when the reservoir volume Vres is too small). This paper investigates the application of a Multi Objective Evolutionary Algorithm (MOEA) to optimise the parameters of the WHRS, including the Vres and depth of discharge (DoD), that affect the WHRS efficiency. Results show that one of the optimum solutions obtained requires the combination of high Vres, high DoD, low water feed in rate, low power external back-up heater and high excess temperature for the HWR to ensure efficiency of the WHRS.

An adaptive health monitoring system or AHMS was developed to improve or add situational awareness to an aircraft, former is for the manned aircraft, and latter is for the unmanned aerial system (UAS). The AHMS provides situational awareness by characterising a notion of health to the aircraft, and uses this health value to perform error detection and error compensation. The notion of health is created by correlating sensors and controller outputs available to the AHMS during flight, and the AHMS does so using an immune-inspired framework (IIF). This creates the AHMS-IIF. This paper presents the results of implementation of the AHMS-IIF on a simple aircraft: the glider system, to see if the AHMS-IIF can provide the situational awareness, which can also increase the endurance of this simple system. The paper shows the AHMS-IIF has provided safer flight outcomes for the glider system. This is judged by the longer duration of flights and higher number of safe landings for the glider, when the glider is flown with the help of the AHMS-IIF at a suitable sampling and accommodation rate. Furthermore, the presented AHMS-IIF is able to achieve its objectives without prior training and optimization of the algorithms, differentiating this framework against other vehicle health management system.

Flight control systems that are tolerant to failures can increase the endurance of an aircraft in case of a failure. The two major types of failure are sensor and actuator failures. This paper focuses on the failure of the gyro sensors in an aircraft. The neuron by neuron (NBN) learning algorithm, which is an improved version of the Levenberg-Marquardt (LM) algorithm, is combined with the fully connected cascade (FCC) neural network architecture to estimate an aircraft's sensor measurements. Compared to other neural networks and learning algorithms, this combination can produce good sensor estimates with relatively few neurons. The estimators are developed and evaluated using flight data collected from the X-Plane flight simulator. The developed sensor estimators can replicate a sensor's measurements with as little as 2 neurons. The results reflect the combined power of the NBN algorithm and the FCC neural network architecture.

This paper presents an ARTMAP-incorporated multi-agent system (MAS) for building heat management, which aims to maintain the desired space temperature defined by the building occupants (thermal comfort management) and improve energy efficiency by intelligently controlling the energy flow and usage in the building (building energy control). Existing MAS typically uses rule-based approaches to describe the behaviours and the processes of its agents, and the rules are fixed. The incorporation of artificial neural network (ANN) techniques to the agents can provide for the required online learning and adaptation capabilities. A three-layer MAS is proposed for building heat management and ARTMAP is incorporated into the agents so as to the facilitate online learning and adaptation capabilities. Simulation results demonstrate that ARTMAP-incorporated MAS provides better (automated) energy control and thermal comfort management for a building environment in comparison to its existing rule-based MAS approach.

The paper presents a multi-objective optimization method for analysing the best mix of renewable and nonrenewable distributed generations (DG) in a distribution network. The method aims at minimising the total cost of the real power generation, line losses and CO 2 emissions, and maximising the benefits from DG installations over a planning horizon of 20 years. The paper proposes new objective functions that take into account the longevity of DG operations as one of its selection criteria. The analysis utilises the Strength Pareto Evolutionary Algorithm 2 (SPEA2) for optimization and MATPOWER for solving the optimal power flow problems.

Good situation awareness is one of the key attributes required to maintain safe flight, especially for an Unmanned Aerial System (UAS). Good situation awareness can be achieved by incorporating an Adaptive Health Monitoring System (AHMS) to the aircraft. The AHMS monitors the flight outcome or flight behaviours of the aircraft based on its external environmental conditions and the behaviour of its internal systems. The AHMS does this by associating a health value to the aircraft's behaviour based on the progression of its sensory values produced by the aircraft's modules, components and/or subsystems. The AHMS indicates erroneous flight behaviour when a deviation to this health information is produced. This will be useful for a UAS because the pilot is taken out of the control loop and is unaware of how the environment and/or faults are affecting the behaviour of the aircraft. The autonomous pilot can use this health information to help produce safer and securer flight behaviour or fault tolerance to the aircraft. This allows the aircraft to fly safely in whatever the environmental conditions. This health information can also be used to help increase the endurance of the aircraft. This paper describes how the AHMS performs its capabilities.

Microgeneration and/or on-site generation from renewable sources, which are generally intermittent, have been pushing the way forward to introduce intelligence to Building Management System (BMS), so as to integrate local energy generation and storage with the public grid and form an intelligent microgrid. This paper aims to develop a property microgrid for the Samuel Lindow Building of the University of Central Lancashire at its Westlakes Campus, West Cumbria, UK. This system covers energy (both electricity and heat) generation from on-site renewable sources and gas, energy storage and energy demand management. Through optimum operation control of the microgrid whilst minimising electrical power transfer from the main grid, this system will enable the property to maximise the benefit of the renewable sources, to improve energy efficiency, to reduce the energy bill, to minimise the impact of the renewable generation on the main grid, and to reduce greenhouse gas emission.

The major challenge to multi-cellular robotics system is how to ensure the system is homeostatically stable. This position paper pro- poses a developmental artificial immune system (dev-AIS) framework that tries to provide and maintain homeostasis to the multi-cellular robotics system. If immunity is defined as the ability to maintain home- ostasis; the dev-AIS framework will be designed based on the under- standing and the abstraction of how different organisms attain for this property through evolution and developmental process. Early form of In- nate Immunity evolve from the predator-and-anti prey relationship of the single-celled organism. Progress in evolution drove the evolution of im- munity from this simple relationship to the development of the immune system in multi-cellular organisms.

This paper presents a novel approach to artificial energy homeostasis in self-reconfigurable modular robots. Using self-monitoring and self-sufficiency principles, energy harvesting in a system of distributed energy sources is proposed. In conjunction with this, we propose an artificial immune system that incorporates on-board power management system for adaptive real time detection of errors which acts as an indicator to a robot to harvest energy from other robots within the local vicinity. Early experiments show that the Artificial Immune System affords a high level of error detection within the power module and will prove to be beneficial in maintaining overall performance of a robotic unit for long periods of time.

The immune system is a key component in the maintenance of host homeostasis. Key actors in this process are cells known as dendritic cells (DCs). An Artificial Immune System based on DCs (known as the Dendritic Cell Algorithm: DCA) is well established in the literature and has been applied in a number of applications. Work in this paper is concerned with the development of an integrated homeostatic system for small, autonomous robotic systems, implemented on a resource limited micro-controller. As a first step, we have modified the DCA to operate in both simulated robotic units, and a resource constrained micro-controller that can operate in an on-line manner. Errors can be introduced into the robotic unit during operation, and these can be detected and then circumvented by the modified DCA.

The SYMBRION project is concerned with the development of super-large swarms of robots that dock with each other and symbiotically share energy and computational resources to form a single artificial organism. This position paper proposes a software architecture, inspired by the lymph node architecture of the biological immune system, that affords homeostasis of individual and collective robotic systems. When robotic units join together into an artificial organism, they form an 'artificial lymphatic system', allowing the robotic units to exchange immunological information. This results in the implementation of the organism homeostasis.

There is now a great interest in exploiting the capabilities of hardware devices; especially the reconfigurable properties of FPGA to implement and study the dynamics of neural networks, specifically a biological inspired neural network. FPGA has a much greater appeal than other modes of implementation because an FPGA implementation offers the flexibility of simple software (re-)configuration in comparison to other digital ASICs. In this paper a hippocampus-inspired spiking neural network is implemented on an FPGA, in order to take advantage of these two characteristics as well as to achieve autonomy for a neuro-controller device.

The hippocampus region may help in the design of a neuro-controller used for path navigation of a robot. The spatial representation of the hippocampus can help provide a spatial mapping of the environment. The representation used is the place cell/place field representation. Place cell represents a population of neurons and place fields provide the spatial mapping of the environment. A place cell fires if the agent is believed to be in the environment for which its respective place field is representing. The hippocampus-inspired neural network model is not only spatially motivated but is also directionally driven. This model will be fully implemented onto an FPGA device to provide autonomy.

This study uses models of pyramidal neurons in the hippocampus to design a hardware spiking neural network neuro-controller model for the purpose of navigation. The neural network model consists of individual neurons modeled using the two-dimensional bio-inspired Izhikevich algorithm. The network is connected according to the connectivity within the hippocampus region, as this region is one of the regions in the brain that is responsible for path navigation. The information processed by the model helps provide navigation and creates memories. The neural network model is intended to be implemented onto a field programmable gate array (FPGA) device. This eliminates the need of an operating system to run the network, thus achieving autonomy.