Omid Ameri Sianaki

Bio: Omid Ameri Sianaki is an academic researcher from Victoria University, Australia. The author has contributed to research in topics: Smart grid & Demand response. The author has an hindex of 10, co-authored 26 publications receiving 286 citations. Previous affiliations of Omid Ameri Sianaki include Curtin University.

A Survey on Conversational Agents/Chatbots Classification and Design Techniques

Abstract: A chatbot can be defined as a computer program, designed to interact with users using natural language or text in a way that the user thinks he is having dialogue with a human. Most of the chatbots utilise the algorithms of artificial intelligence (AI) in order to generate required response. Earlier chatbots merely created an illusion of intelligence by employing much simpler pattern matching and string processing design techniques for their interaction with users using rule-based and generative-based models. However, with the emergence of new technologies more intelligent systems have emerged using complex knowledge-based models. This paper aims to discuss chatbots classification, their design techniques used in earlier and modern chatbots and how the two main categories of chatbots handle conversation context.

Intelligent Decision Support System for Including Consumers' Preferences in Residential Energy Consumption in Smart Grid

Abstract: Smart Grid is a novel initiative the aim of which is to deliver energy to the users and also to achieve consumption efficiency by means of two-way communication. The Smart Grid architecture is a combination of various hardware devices, management and reporting software tools that are combined within an ICT infrastructure. This infrastructure is needed to make the smart grid sustainable, creative and intelligent. One of the main goals of Smart Grid is to achieve Demand Response(DR) by increasing the end users’ participation in decision making and increasing the awareness that will lead them to manage their energy consumption in an efficient way. Approaches proposed in the literature achieve demand response at the different levels of the Smart Grid, but no approach focuses on the users’ point of view at the home level on a continuous basis and in an intelligent way to achieve demand response. In this paper, we develop such an approach by which demand response can be achieved on a continuous basis at the home level. To achieve this, the dynamic notion of price will be utilized to develop an intelligent decision-making model that will assist the users in achieving demand response.

A Knapsack problem approach for achieving efficient energy consumption in smart grid for endusers' life style

Abstract: In order to achieve an efficient energy consumption level in the residential sector of a smart grid, the end-users are equipped with various smart home energy controller technologies. The devices are provided to inform the consumers about their consumption pattern by showing or sending different kinds of consumptional information to them. This kind of information is provided to assist them in making decisions about altering their consumption behaviour or to urge them to modify their life style during peak hours. We propose that the energy home controllers should offer preferred and optimal scenarios to support end-users when making a decision about their consumption. Effective scenarios should emerge from consumer's life style and preferences. In this paper, we will apply AHP methodology to quantify the consumer's preferences for using appliances during peak periods when the price has increased, and use the Knapsack problem approach to achieve the optimal solution for managing the appliances. With this approach, not only will the cost of electricity not escalate during peak hours, but also user preferences, satisfaction and minimum change to current life style will be considered.

A fuzzy TOPSIS approach for home energy management in smart grid with considering householders' preferences

Abstract: It is expected that demand response programs will be designed to decrease electricity consumption or shift it from on-peak to off-peak periods depending on consumers' preferences and lifestyles. This paper demonstrates a fuzzy TOPSIS decision-making approach to quantify and evaluate consumers' preferences at the micro-level when using electrical devices according to a real-time price scheme of demand response in order to best manage the use of appliances. This enables and supports householders to maximize their participation in demand response programs.

[ACM Press the 2nd ACM Workshop - Zurich, Switzerland (2010.11.02-2010.11.02)] Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building - BuildSys \'10 - Occupancy-driven energy management for smart building automation

Abstract: Buildings are among the largest consumers of electricity in the US. A significant portion of this energy use in buildings can be attributed to HVAC systems used to maintain comfort for occupants. In most cases these building HVAC systems run on fixed schedules and do not employ any fine grained control based on detailed occupancy information. In this paper we present the design and implementation of a presence sensor platform that can be used for accurate occupancy detection at the level of individual offices. Our presence sensor is low-cost, wireless, and incrementally deployable within existing buildings. Using a pilot deployment of our system across ten offices over a two week period we identify significant opportunities for energy savings due to periods of vacancy. Our energy measurements show that our presence node has an estimated battery lifetime of over five years, while detecting occupancy accurately. Furthermore, using a building simulation framework and the occupancy information from our testbed, we show potential energy savings from 10% to 15% using our system.

The Future of Healthcare Internet of Things: A Survey of Emerging Technologies

Abstract: The impact of the Internet of Things (IoT) on the advancement of the healthcare industry is immense. The ushering of the Medicine 4.0 has resulted in an increased effort to develop platforms, both at the hardware level as well as the underlying software level. This vision has led to the development of Healthcare IoT (H-IoT) systems. The basic enabling technologies include the communication systems between the sensing nodes and the processors; and the processing algorithms for generating an output from the data collected by the sensors. However, at present, these enabling technologies are also supported by several new technologies. The use of Artificial Intelligence (AI) has transformed the H-IoT systems at almost every level. The fog/edge paradigm is bringing the computing power close to the deployed network and hence mitigating many challenges in the process. While the big data allows handling an enormous amount of data. Additionally, the Software Defined Networks (SDNs) bring flexibility to the system while the blockchains are finding the most novel use cases in H-IoT systems. The Internet of Nano Things (IoNT) and Tactile Internet (TI) are driving the innovation in the H-IoT applications. This paper delves into the ways these technologies are transforming the H-IoT systems and also identifies the future course for improving the Quality of Service (QoS) using these new technologies.

Home energy management systems: A review of modelling and complexity

Abstract: Innovations in the residential sector are required to reduce environmental impacts, as the sector is a contributor to greenhouse gas emissions. The increasing demand for electricity and the emergence of smart grids have presented new opportunities for home energy management systems (HEMS) in demand response markets. HEMS are demand response tools that shift and curtail demand to improve the energy consumption and production profile of a dwelling on behalf of a consumer. HEMS usually create optimal consumption and productions schedules by considering multiple objectives such as energy costs, environmental concerns, load profiles and consumer comfort. The existing literature has presented several methods, such as mathematical optimization, model predictive control and heuristic control, for creating efficient operation schedules and for making good consumption and production decisions. However, the effectiveness of the methods in the existing literature can be difficult to compare due to diversity in modelling parameters, such as appliance models, timing parameters and objectives. The present chapter provides a comparative analysis of the literature on HEMS, with a focus on modelling approaches and their impact on HEMS operations and outcomes. In particular, we discuss a set of HEMS challenges such as forecast uncertainty, modelling device heterogeneity, multi-objective scheduling, computational limitations, timing considerations and modelling consumer well-being. The presented work is organized to allow a reader to understand and compare the important considerations, approaches, nomenclature and results in prominent and new literary works without delving deeply into each one.