Energy Optimization and Management of Demand Response Interactions in a Smart Campus

The proposed framework enables innovative power management in smart campuses, integrating local renewable energy sources, battery banks and controllable loads and supporting Demand Response interactions with the electricity grid operators. The paper describes each system component: the Energy Management System responsible for power usage scheduling, the telecommunication infrastructure in charge of data exchanging and the integrated data repository devoted to information storage. We also discuss the relevant use cases and validate the framework in a few deployed demonstrators

Mainstreaming zero carbon : lessons for built-environment education and training

Education and training are identified as a key means of reducing carbon emissions from buildings to help address the climate emergency. Institutional, industry and organisational responses are shown to be failing in this regard. This editorial introduces the themes and individual papers in the special issue and then explores the current state of the art through pedagogy, theory, training, policy, practice and standards. These areas are interrogated through three fundamental questions. How can education and training be rapidly changed to ensure the creation of zero-carbon built environments? How can this transition be implemented successfully? What positive examples and models can be drawn upon or adapted? In proposing an agenda for change, a new approach to education is set out which combines learning outcomes with new standards and personal values within a continual questioning and holding to account of all stakeholders involved through evidenced outcomes. This draws on evidence from the special issue and Capability Theory which allies competency with personhood to create capability through agency. The process to make this change requires: (1) government intervention, to ensure that the lowest common denominator is zero-carbon best practice within a negotiated, holistic approach to developing the built environment sustainably; (2) new ethical, interdisciplinary and collective educational working practices underpinned by new pedagogical theory and accreditation processes; and (3) rapid auditing and upskilling in climate literacy to bring pressure to bear on governments and institutions to carry out reforms

Internet of Things and Intelligent Technologies for Efficient Energy Management in a Smart Building Environment

Internet of Things (IoT) is attempting to transform modern buildings into energy efficient, smart, and connected buildings, by imparting capabilities such as real-time monitoring, situational awareness and intelligence, and intelligent control. Digitizing the modern day building environment using IoT improves asset visibility and generates energy savings. This dissertation provides a survey of the role, impact, and challenges and recommended solutions of IoT for smart buildings. It also presents an IoT-based solution to overcome the challenge of inefficient energy management in a smart building environment. The proposed solution consists of developing an Intelligent Computational Engine (ICE), composed of various IoT devices and technologies for efficient energy management in an IoT driven building environment. ICE’s capabilities viz. energy consumption prediction and optimized control of electric loads have been developed, deployed, and dispatched in the Real-Time Power and Intelligent Systems (RTPIS) laboratory, which serves as the IoT-driven building case study environment. Two energy consumption prediction models viz. exponential model and Elman recurrent neural network (RNN) model were developed and compared to determine the most accurate model for use in the development of ICE’s energy consumption prediction capability. ICE’s prediction model was developed in MATLAB using cellular computational network (CCN) technique, whereas the optimized control model was developed jointly in MATLAB and Metasys Building Automation System (BAS) using particle swarm optimization (PSO) algorithm and logic connector tool (LCT), respectively. It was demonstrated that the developed CCN-based energy consumption prediction model was highly accurate with low error % by comparing the predicted and the measured energy consumption data over a period of one week. The predicted energy consumption values generated from the CCN model served as a reference for the PSO algorithm to generate control parameters for the optimized control of the electric loads. The LCT model used these control parameters to regulate the electric loads to save energy (increase energy efficiency) without violating any operational constraints. Having ICE’s energy consumption prediction and optimized control of electric loads capabilities is extremely useful for efficient energy management as they ensure that sufficient energy is generated to meet the demands of the electric loads optimally at any time thereby reducing wasted energy due to excess generation. This, in turn, reduces carbon emissions and generates energy and cost savings. While the ICE was tested in a small case-study environment, it could be scaled to any smart building environment