Saleh Seyedzadeh

Bio: Saleh Seyedzadeh is an academic researcher from University of Strathclyde. The author has contributed to research in topics: Code division multiple access & Chip. The author has an hindex of 13, co-authored 49 publications receiving 586 citations. Previous affiliations of Saleh Seyedzadeh include University of Malaya & Universiti Putra Malaysia.

Machine learning for estimation of building energy consumption and performance: a review

Abstract: Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy efficiency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most effective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy efficiency at a very early design stage. On the other hand,efficient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, artificial intelligence (AI) in general and machine learning (ML) techniques in specific terms have been proposed for forecasting of building energy consumption and performance. This paper provides a substantial review on the four main ML approaches including artificial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance.

Geostatistics For Environmental Scientists

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Hybrid SDN Networks: A Survey of Existing Approaches

Abstract: Software defined networking (SDN) decouples the control plane from the data plane of forwarding devices. This separation provides several benefits, including the simplification of network management and control. However, due to a variety of reasons, such as budget constraints and fear of downtime, many organizations are reluctant to fully deploy SDN. Partially deploying SDN through the placement of a limited number of SDN devices among legacy (traditional) network devices, forms a so-called hybrid SDN network. While hybrid SDN networks provide many of the benefits of SDN and have a wide range of applications, they also pose several challenges. These challenges have recently been addressed in a growing body of literature on hybrid SDN network structures and protocols. This paper presents a comprehensive up-to-date survey of the research and development in the field of hybrid SDN networks. We have organized the survey into five main categories, namely hybrid SDN network deployment strategies, controllers for hybrid SDN networks, protocols for hybrid SDN network management, traffic engineering mechanisms for hybrid SDN networks, as well as testing, verification, and security mechanisms for hybrid SDN networks. We thoroughly survey the existing hybrid SDN network studies according to this taxonomy and identify gaps and limitations in the existing body of research. Based on the outcomes of the existing research studies as well as the identified gaps and limitations, we derive guidelines for future research on hybrid SDN networks.

A review of strategies for building energy management system: Model predictive control, demand side management, optimization, and fault detect & diagnosis

Abstract: Building energy use is expected to grow by more than 40% in the next 20 years. Electricity remains the largest energy source consumed by buildings, and that demand is growing. To mitigate the impact of the growing demand, strategies are needed to improve buildings' energy efficiency. In residential buildings home appliances, water, and space heating are answerable for the increase of energy use, while space heating and other miscellaneous equipment are behind the increase of energy utilization in non-residential buildings. Building energy management systems support building managers and proprietors to increase energy efficiency in modern and existing buildings, non-residential and residential buildings can benefit from building energy management system to decrease energy use. Base on the type of building, different management strategies can be used to achieve energy savings. This paper presents a review of management strategies for building energy management systems for improving energy efficiency. Different management strategies are investigated in non-residential and residential buildings. Following this, the reviewed researches are discussed in terms of the type of buildings, building systems, and management strategies. Lastly, the paper discusses future challenges for the increase of energy efficiency in building energy management system.

Towards integrated performance-driven generative design tools

Abstract: Performance-driven generative design methods are capable of producing concepts and stimulating solutions based on robust and rigorous models of design conditions and performance criteria. Using generative methods, the computer becomes a design generator in addition to its more conventional role as draftsperson, visualizor, data checker and performance analyst. To enable designers to readily develop meaningful input models, this paper describes a preliminary integration of a generative structural design system, eifForm, and an associative modeling system, Generative Components, through the use of XML models. An example is given involving generation of 20 lightweight, cantilever roof trusses for a saddle shaped stadium roof modeled in Generative Components. Synergies between the two systems and future extensions are discussed.