Smart Home with energy saving using particle swarm opt.pptx

Smart Home with Energy Saving

1. Smart Home with Energy Saving using Demand Side Management through Particle Swarm Optimization and Home Assistant Djoni H. Setiabudi Resmana Lim Michael Alexander IoTaIS 2023

2. Introduction -Problem Smart home is part of Internet of Things which makes human life easier. Problem: every smart home with different brand has its own application must be installed, it is necessary to use only 1 application for all brands ( Bardi , Millenix , Sonoff , EzViz,etc .) Each house has limited electric power , power supply can be disrupted if the maximum capacity is exceeded, so it is necessary to arrange that all electrical equipment only turns on when needed by setting priorities and interests

3. Introduction - problem Hence, a system is needed to : Controlling all smart home devices using 1 application Saving Energy to save electricity usage Many studies have been conducted saving energy , but not combining with Home Assistant

4. Previous Studies Many previous studies focused on saving enery : Zhu et al. [2021] , Lin & Hu [2018], Li et al.[2016], Alfafa and Bilfaqih's [2022], Malik and Kim [2018] 2 of them also use Particle Swarm Optimization : Zhu et al. and Malik and Kim But all of them have the weaknesess : No user preferences/ urgency No priorities No warning of electrical overload limitation Not easy to connect many smart home brands

5. Research Gap /Contribution Research gap / the contribution of this research over the previous studies is : The integration of the Home Assistant framework and Particle Swarm Optimization . Home Assistant : controlling all new smart devices from different brands using only 1 Application only Utilizes Particle Swarm Optimization (PSO) to : Saving energy by determine Priorities dan Interest of all connected device and give warning if kWh and watt usage exceed the limits

6. What is Home Assistant ? Home Assistant is an IoT open-source software can be programmed through a smartphone application or the Home Assistant website

7. What is Demand Site Management? Is a strategic approach employed by electric utilities to regulate and optimize the consumption of electricity . DSM emphasizes managing the efficiency consumer demand for energy, with the characteristics : Control of Electricity Demand Energy Conservation Strategy Continuous Monitoring Consumer Participation Modification of Consumer Demand PSO is used to optimize the use of energy saving

8. What is Particle Swarm Optimization (PSO)? Particle Swarm Optimization (PSO) is one of the many optimization algorithms used to make decisions in solving a problem

9. Scheduling and Load Control of Electric Power Using PSO The PSO method is employed to determine which devices are allowed to be turned on. Users can specify : usage preferences priorities urgency for these devices. The PSO method attempts to make decisions to turn on or off electrical devices according to the constraint of installed power and considering users' scheduling preferences for activation.

10. Methods The PSO method attempts to make decisions to turn on or off electrical devices according to the constraint of installed power and considering users' scheduling preferences for activation, Urgency & the Power Availability

11. Smart Home System with Home Assistant and PSO Home Assistant

12. Experimental Results History of Usage

13. Experimental Results History of Energy Used

14. Experimental Results Schedular

15. Experimental Results Performance Testing

16. Experimental Results Scheduler Button Abrupt Testing

17. Experimental Results PSO And Non PSO 1 Week Testing PSO Leading on most of the day in saving energy

18. Discussion Based on testing conducted over a week, PSO produces various combinations while still prioritizing calculations of wattage, urgency, and priority . A stopping criteria is used in PSO to terminate if there is no change in gbest for a certain number of iterations, although there might still be iterations and particles

19. Conclusions Utilizing the Home Assistant Framework can be modified to use 1 application for controlling smart devices of different brands. The results of the one-week experimentation for both PSO and non-PSO, reveal that on Monday, the kWh consumption is draw, while on Tuesday, Thursday, Friday, and Saturday, PSO yields more energy-efficient consumption. Therefore, the electricity saving percentage using PSO is 14% compared to non-PSO . For future research , it can be done by trying other algorithms besides PSO to save energy.