ITERL: A Wireless Adaptive System for Efficient Road Lighting

This work presents the development and construction of an adaptive street lighting system that improves safety at intersections, which is the result of applying low-power Internet of Things (IoT) techniques to intelligent transportation systems. A set of wireless sensor nodes using the Institute of Electrical and Electronics Engineers (IEEE) 802.15.4 standard with additional internet protocol (IP) connectivity measures both ambient conditions and vehicle transit. These measurements are sent to a coordinator node that collects and passes them to a local controller, which then makes decisions leading to the streetlight being turned on and its illumination level controlled. Streetlights are autonomous, powered by photovoltaic energy, and wirelessly connected, achieving a high degree of energy efficiency. Relevant data are also sent to the highway conservation center, allowing it to maintain up-to-date information for the system, enabling preventive maintenance.Consejería de Fomento y Vivienda Junta de Andalucía G-GI3002 / IDIOFondo Europeo de Desarrollo Regional G-GI3002 / IDI

Personal lighting control with occupancy and daylight adaptation

Personal control with occupancy and daylight adaptation is considered in a lighting system with multiple luminaires. Each luminaire is equipped with a co-located occupancy sensor and light sensor. Using sensor feedback and user input, the central controller determines dimming values of the luminaires using an optimization framework. The performance of the proposed controllers is compared with a reference stand-alone controller under different simulation scenarios in an office lighting syste

DC Network Indoor and Outdoor LED Lighting

LED lighting products have become a significant revolution in this technological sector. These components are, by nature, digital emitters created with semiconductor crystals that are powered with very low voltage and direct current (DC). Under these conditions, they have become one of the most relevant actors in the present tendency that is recovering the DC as the channel to transport and distribute energy and is reinforcing the photovoltaic (PV) panels as a relevant sustainable energy source that allows to improve the efficiencies of all types of lighting installations with the local self-generated energy. An analysis of the working principles of this component and the mechanism implemented for their control as lighting equipment to be powered with both conventional alternate current (AC) and DC is presented. A specific differentiation is done upon indoor and outdoor applications where new standards and regulations, specific technical procedures, and singular experimental project descriptions are detailed. The results expose the advantages and difficulties of implementation of this new DC paradigm, the main conclusion obtained up to this moment, and trends of future evolution

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