A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

A/C Energy Management and Vehicle Cabin Thermal Comfort Control

This paper introduces a novel multi-objective controller which regulates A/C system operation in a trade-off between vehicle cabin comfort and fuel consumption for a conventional vehicle with internal combustion engine. The controller has been developed and tested in a simulated environment, where an energy-based model of the A/C system is combined with a thermal dynamic model of the cabin which considers heat transfer to the environment. The control algorithm proposed herein is compared with two widely used control techniques in the industry, respectively the thermostat and PI control, under different driving cycles. This novel method is implementable in real-time, and simulation results show a reduction of up to 2% in A/C system fuel consumption compared to existing methods with similar thermal performance

Design and evaluation of a body temperature controlled Air-conditioning system

While remote sensing technologies for airconditioners have been available for some time, no research has been done on airconditioner remote sensing of the body. This thesis looks at the opportunities for remotely sensing body temperature from the wrist. The goal of this report was to evaluate any potential energy savings to be had for airconditioners by utilising this measure of the body. A prototype was designed emphasising factors such as size, weight and energy consumption/battery life. The prototype was then evaluated for success by comparison with baseline energy use and the observance of a reduction in the coefficient of determination between outside air temperature and energy use. While dramatic energy savings were not realised due to the simplistic nature of the prototype, a saving of almost a kilowatt hour for sub 35ºC days was able to be achieved. These results show the promise that body temperature sensing offers

Thermal Comfort Aspects of Solar Gains during the Heating Season

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Analysis and Application of Advanced Control Strategies to a Heating Element Nonlinear Model

open4siSustainable control has begun to stimulate research and development in a wide range of industrial communities particularly for systems that demand a high degree of reliability and availability (sustainability) and at the same time characterised by expensive and/or safety critical maintenance work. For heating systems such as HVAC plants, clear conflict exists between ensuring a high degree of availability and reducing costly maintenance times. HVAC systems have highly non-linear dynamics and a stochastic and uncontrollable driving force as input in the form of intake air speed, presenting an interesting challenge for modern control methods. Suitable control methods can provide sustainable maximisation of energy conversion efficiency over wider than normally expected air speeds and temperatures, whilst also giving a degree of “tolerance” to certain faults, providing an important impact on maintenance scheduling, e.g. by capturing the effects of some system faults before they become serious.This paper presents the design of different control strategies applied to a heating element nonlinear model. The description of this heating element was obtained exploiting a data driven and physically meaningful nonlinear continuous time model, which represents a test bed used in passive air conditioning for sustainable housing applications. This model has low complexity while achieving high simulation performance. The physical meaningfulness of the model provides an enhanced insight into the performance and functionality of the system. In return, this information can be used during the system simulation and improved model based and data driven control designs for tight temperature regulation. The main purpose of this study is thus to give several examples of viable and practical designs of control schemes with application to this heating element model. Moreover, extensive simulations and Monte Carlo analysis are the tools for assessing experimentally the main features of the proposed control schemes, in the presence of modelling and measurement errors. These developed control methods are also compared in order to evaluate advantages and drawbacks of the considered solutions. Finally, the exploited simulation tools can serve to highlight the potential application of the proposed control strategies to real air conditioning systems.openTurhan, T.; Simani, S.; Zajic, I.; Gokcen Akkurt, G.Turhan, T.; Simani, Silvio; Zajic, I.; Gokcen Akkurt, G

INDOOR ENVIRONMENTAL QUALITY (IEQ) AND BUILDING ENERGY OPTIMIZATION THROUGH MODEL PREDICTIVE CONTROL (MPC)

This dissertation aims at developing a novel and systematic approach to apply Model Predictive Control (MPC) to improve energy efficiency and indoor environmental quality in office buildings. Model predictive control is one of the advanced optimal control approaches that use models to predict the behavior of the process beyond the current time to optimize the system operation at the present time. In building system, MPC helps to exploit buildings’ thermal storage capacity and to use the information on future disturbances like weather and internal heat gains to estimate optimal control inputs ahead of time. In this research the major challenges of applying MPC to building systems are addressed. A systematic framework has been developed for ease of implementation. New methods are proposed to develop simple and yet reasonably accurate models that can minimize the MPC development effort as well as computational time. The developed MPC is used to control a detailed building model represented by whole building performance simulation tool, EnergyPlus. A co-simulation strategy is used to communicate the MPC control developed in Matlab platform with the case building model in EnergyPlus. The co-simulation tool used (MLE+) also has the ability to talk to actual building management systems that support the BACnet communication protocol which makes it easy to implement the developed MPC control in actual buildings. A building that features an integrated lighting and window control and HVAC system with a dedicated outdoor air system and ceiling radiant panels was used as a case building. Though this study is specifically focused on the case building, the framework developed can be applied to any building type. The performance of the developed MPC was compared against a baseline control strategy using Proportional Integral and Derivative (PID) control. Various conventional and advanced thermal comfort as well as ventilation strategies were considered for the comparison. These include thermal comfort control based on ASHRAE comfort zone (based on temperature and relative humidity) and Predicted Mean Vote (PMV) and ventilation control based on ASHRAE 62.1 and Demand Control Ventilation (DCV). The building energy consumption was also evaluated with and without integrated lighting and window blind control. The simulation results revealed better performance of MPC both in terms of energy savings as well as maintaining acceptable indoor environmental quality. Energy saving as high as 48% was possible using MPC with integrated lighting and window blind control. A new critical contaminant - based demand control ventilation strategy was also developed to ensure acceptable or higher indoor air quality. Common indoor and outdoor contaminants were considered in the study and the method resulted in superior performance especially for buildings with strong indoor or outdoor contaminant sources compared to conventional CO2 - based demand control ventilation which only monitors CO2 to vary the minimum outdoor air ventilation rate

Feed-Forward Neural Network (FFNN) Based Optimization Of Air Handling Units: A State-Of-The-Art Data-Driven Demand-Controlled Ventilation Strategy

Indiana University-Purdue University Indianapolis (IUPUI)Heating, ventilation and air conditioning systems (HVAC) are the single largest consumer of energy in commercial and residential sectors. Minimizing its energy consumption without compromising indoor air quality (IAQ) and thermal comfort would result in environmental and financial benefits. Currently, most buildings still utilize constant air volume (CAV) systems with on/off control to meet the thermal loads. Such systems, without any consideration of occupancy, may ventilate a zone excessively and result in energy waste. Previous studies showed that CO2-based demand-controlled ventilation (DCV) methods are the most widely used strategies to determine the optimal level of supply air volume. However, conventional CO2 mass balanced models do not yield an optimal estimation accuracy. In this study, feed-forward neural network algorithm (FFNN) was proposed to estimate the zone occupancy using CO2 concentrations, observed occupancy data and the zone schedule. The occupancy prediction result was then utilized to optimize supply fan operation of the air handling unit (AHU) associated with the zone. IAQ and thermal comfort standards were also taken into consideration as the active constraints of this optimization. As for the validation, the experiment was carried out in an auditorium located on a university campus. The results revealed that utilizing neural network occupancy estimation model can reduce the daily ventilation energy by 74.2% when compared to the current on/off control