Autonomous Efficient Whole-Home Heating and Cooling System - Paper Example

This paper aims at outlining a whole-home heating/cooling system that is autonomous and efficient. Among the details to be incorporated along with this discussion and centralized and distributed control systems and decision-making algorithms of techniques; using fuzziness decision. Most of the times whole-home heating/cooling systems have been installed but they have not lasted for a long while due to inefficiencies in their production from companies where they are installed. Therefore, there has been a great urge for development of systems which are autonomous and efficient. An autonomous system is one which does not require manual input in terms of labor for it to work. The technological advancements have enhanced inventions that come along with many advantages as they are aimed at reducing the amount of physical energy employed in operating them. Energy needs to be used conservatively, in a manner that is more efficient; it is important to note the fact that energy cannot be created or destroyed.

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One way in which an autonomous efficient whole-home heating/cooling system can be developed is by a device which occurs in the form of an intelligent remote control whose sole purpose is controlling the ambient condition of a house. The intelligent remote control device is should be capable of controlling in two different ways- autonomous and semiautonomous. Appliances dimming levels in this setup will be tuned with an optimized PID controller. To control the intensity of light and levels of humidity of the room, a fundamental development of a hardware prototype is essential. Calculation of the PID controller tuning parameters is done with the help of the systems overall transfer function. The systems overall transfer function entails the feedback signal from the sensor, optimization algorithm objective function, and the appliances (intelligent remote control) transfer function. To optimize the tuning parameters for the PID, a Particle Swarm Optimization algorithm is required and results show that more energy efficiency occurs with proposed system in comparison to the conventional systems. With all these in place, expectations are that the algorithm of the proposed PSO-PID for designed system will save on more energy as a result of the intelligent hybrid remote control (Singh, Kuchhal, Gehlot, & Choudhury, 2016).

Whole-home systems usually are easy to setup and always require one energy meter. This being the case, therefore, the only way to make sure that the right reading is the one recorded, one has to make sure that the energy meter is working accurately. Proper working of the energy meter will reduce on the cost of the wasted energy that may come along with regards to a malfunctioning energy meter. Many are the cases when the energy meter becomes faulty due to usage for a longer time, therefore, the energy meter needs to be regularly checked and replaced so that the energy consumption readings are not always biased. Multitasking of an energy meter that runs a whole-home system can also have significant implication on the energy consumption rate, in that when there is a high flow of energy than the energy meter can contain, then, the energy meter loses grip and the readings will later be approximations and not real values. Before choosing an energy meter, one needs to consider the total amount of energy that will be consumed by the whole-home system and find out the energy meter that works within the range of the consumption of the whole-home system. This is an effective way of conserving energy.

Ultimately, it is important to note that autonomous systems are not concerned with efforts of human beings which most of the time are prone to neglect and error. Automatic systems work in such a manner that there is a shift in the aspect which they function from the human aspect to the aspect of the system which now moves towards intelligent-self controlling systems development. Intelligent-self controlling systems are aware of context and above all, energy efficient. Automatic system automates the control of whole-home heating systems through occupancy prediction; it designates the highest energy consuming system in the household as the heating system. In this case, a PreHeat is always recommended other than leaving the whole-home heating system to the householders, for example, through manually programmable thermostats. The PreHeat automatically predicts when the heating system needs to be turned on and when the system needs to be turned off. PreHeat functions in a manner such that it reduces on the MissTime, which is basically the time when the house is occupied but is not warmed; the system does this by sensing the occupancy of the household and by using the historical data in estimating the future usage. Other than the physical sensory hardware, for example motion and temperature sensors, at PreHeats heart there is always a prediction algorithm that reacts to occupants when they enter a room and it is also capable of predicting when an occupied room will be used next (Zhen, 2016).

Despite PreHeat, there exist other occupancy sensing techniques which use similar prediction models and motion sensors to turn heating, ventilation, and air conditioning (HVAC) systems on and off simultaneously. A wireless sensor network that is always aware of the presence of householders including other physical parameters such as temperature and light; the wireless network also enhances the system to be able to modify the systems behavior in accordance with environmental changes. Another efficient way of autonomously heating/cooling a whole-home is by using DC nanogrids which are effective solutions to integrating numerous distributed energy renewable resources, DC loads, and energy storage. Setting up three-layered novel automation architecture for DC nanogrid is a basic requirement for optimal functionality of the DC nanogrid. The bottom layer which is mainly the converter level control needs to be fully decentralized in order for it to allow a plug and play functionality without prior need of horizontal communication. The middle layer functions to optimize energy resources usage and storage as well as the usage of heating/cooling devices based on a Multi-Agent System. Finally, the top layer of the DC nanogrid functions as the user interface and the port of communication to the Energy Network Operator; the purpose of this is to enable smart grid capabilities for example, demand side management, grid support, and demand response. This novel automation architecture requires combination of dynamic direct heating systems with heat pumps that are highly efficient (Riccobono, et al, 2016).

Additionally, deploying an occupancy sensing electronic thermostat comprised basically of a thermostat body which has an exterior front surface that is curved, a dot matrix display that is mounted within the body of the thermostat viewable by the user standing in front of the front surface, a passive infrared sensor aimed at measuring infrared energy, a Fresnel lens member that is shaped such that it has a smooth outer surface which extends across the thermostats body exterior front surface portion; the Fresnel lens member focuses infrared energy onto a passive infrared sensor, a first temperature sensor which is positioned behind the Fresnel lens member whose purpose is making measurements of temperature that are used for calculating ambient temperature, a second temperature sensor which is positioned within the body of the thermostat with close proximity than the first temperature sensor to the heat generating component(s) that are within the thermostat body, and a microprocessor which is programmed to detect occupancy that is based at least in part on the measurements made by the passive infrared sensor (Filson, Daniels, & Huppi, 2013).

References

Filson, J. B., Daniels, E. B., & Huppi, B. (2013). U.S. Patent No. 8,558,179. Washington, DC: U.S. Patent and Trademark Office.

Riccobono, A., Ferdowsi, M., Hu, J., Wolisz, H., Jahangiri, P., Muller, D., ... & Monti, A. (2016, April). Next generation automation architecture for DC smart homes. In Energy Conference (ENERGYCON), 2016 IEEE International (pp. 1-6). IEEE.

Singh, R., Kuchhal, P., Gehlot, A., & Choudhury, S. (2016). Design and Implementation of Energy Efficient Home Automation System. Indian Journal of Science and Technology, 9(6).

Zhen, S. Home Energy Conservation: Methods and Approaches.