Intelligent Decision Support System for Energy Management in Demand Response Programs and Residential and Industrial Sectors of the Smart Grid

This PhD thesis addresses the complexity of the energy efficiency control problem in residential and industrial customers of Smart electrical Grid, and examines the main factors that affect energy demand, and proposes an intelligent decision support system for applications of demand response. A multi criteria decision making algorithm is combined with a combinatorial optimization technique to assist energy managers to decide whether to participate in demand response programs or obtain energy from distributed energy resources

Versatile Energy Scheduler Compatible with Autonomous Demand Response for Home Energy Management in Smart Grid: A System of Systems Approach

The future home energy management system (HEMS) in smart grid will need to include contractual grid regulations imposed by the utility while also taking into consideration the domestic users’ comfort, preferences, budget, and security. The emerging autonomous demand response (ADR) programs have initiated steps in utilizing sophisticated software algorithms for the scheduling and optimization of HEMS. This paper proposes a system of systems approach as a versatile energy scheduling system that takes into account the components, characteristics and methodologies required for achieving an efficient level of energy consumption in the residential sector of the smart grid

Machine Learning Applications: The Past and Current Research Trend in Diverse Industries

Dramatic changes in the way we collect and process data has facilitated the emergence of a new era by providing customised services and products precisely based on the needs of clients according to processed big data. It is estimated that the number of connected devices to the internet will pass 35 billion by 2020. Further, there has also been a massive escalation in the amount of data collection tools as Internet of Things devices generate data which has big data characteristics known as five V (volume, velocity, variety, variability and value). This article reviews challenges, opportunities and research trends to address the issues related to the data era in three industries including smart cities, healthcare and transportation. All three of these industries could greatly benefit from machine learning and deep learning techniques on big data collected by the Internet of Things, which is named as the internet of everything to emphasise the role of connected devices for data collection. In the smart grid portion of this paper, the recently developed deep reinforcement learning techniques and their applications in Smart Cities are also presented and reviewed

Software-defined application-specific traffic management for wireless body area networks

Wireless body area networks (WBANs) are usually used to collect and monitor health-related information for both critical and non-critical patients. However, the traditional WBAN communication framework is unable to guarantee the successful delivery of critical information due to a lack of administrative control and priority support for emergency data. To overcome these issues, this paper proposes a novel software-defined networking (SDN)-based WBAN (SDWBAN) framework for application-specific traffic management. An application classification algorithm and a packet flow mechanism are developed by incorporating SDN principles with WBAN to effectively manage complex and critical traffic in the network. Furthermore, a Sector-Based Distance (SBD) protocol is designed and utilized to facilitate the SDWBAN communication framework. Finally, the proposed SDWBAN framework is evaluated through the CASTALIA simulator in terms of Packet Delivery Ratio (PDR) and latency. The experimental outcomes show that the proposed system achieves high throughput and low latency for emergency traffic in SDWBANs

A decision support algorithm for assessing the engagement of a demand response program in the industrial sector of the smart grid

In the industrial sector of the smart grid (SG), a demand response program (DRP) is offered to consumers to motivate them to shift their demand for electricity to the off-peak period. DRP can cause a dilemma for industrial consumers when energy load is decreased since it may disrupt the production process and they may consequently incur losses. Hence, industrial units may choose to accept or reject a DRP. If they choose to engage in a DRP, they may use the available back-up on-site energy resources to access the required amount of energy. Hence, any decision about load curtailment requires a comprehensive assessment of all layers of production and operational management. This paper utilises several methodologies to evaluate the effects of DRP engagement on operational management. Firstly, the Delphi method is employed for extracting and identifying twenty-six criteria embedded in ten operational and production management factors. Secondly, based on these criteria, the production equipment is ranked using the TOPSIS method. This ranking shows which equipment will have less impact on the organisation's profit as a result of participating in a DRP; but, it will not support production and energy planning which is affected by DRP engagement. So, thirdly, a linear programming (LP) model in a discrete scheduling time horizon is proposed which considers the TOPSIS method output and all the constraints imposed by the DRP and the production resources. Finally, based on the proposed methodology, a decision-making algorithm is designed to assist the operation and energy managers to decide whether to accept or reject the offer to engage in a DRP and if they decide to participate, how to best utilize the available distributed energy resources to regain the energy lost. The main contribution of this paper is the proposed methodology which combines the outcome of the Delphi and TOPSIS methods with a linear optimisation model, the effectiveness of which is clearly demonstrated by the sensitivity analysis