Open AccessDemand Based Cost Optimization of Electric Bills for Household Users
Author(s) -
Nabeel Tawalbeh,
Mohammad Malkawi,
Hanan Mohammad Abusamaha,
Sahban W. Alnaser
Publication year - 2021
Publication title -
international journal of communication networks and information security
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.216
H-Index - 20
eISSN - 2076-0930
pISSN - 2073-607X
DOI - 10.54039/ijcnis.v13i3.5111
Subject(s) - computer science , energy (signal processing) , automotive engineering , factory (object oriented programming) , energy consumption , arduino , demand response , control (management) , voltage , response time , real time computing , simulation , embedded system , engineering , electricity , electrical engineering , artificial intelligence , operating system , statistics , mathematics , programming language
- Internet of Things (IoT) is increasingly becoming the vehicle to automate, optimize and enhance the performance of systems in the energy, environment, and health sectors. In this paper, we use Wi-Fi wrapped sensors to provide online and in realtime the current energy consumptions at a device level, in a manner to allow for automatic control of peak energy consumption at a household, factory level, and eventually at a region level, where a region can be defined as an area supported by a distinct energy source. This allows to decrease the bill by avoiding heavily and controllable loads during high tariff slice and/or peak period per household and to optimize the energy production and distribution in a given region. The proposed model relies on adaptive learning techniques to help adjust the current load, while taking into consideration the actual and real need of the consumer. The experiments used in this study makes use of current and voltage sensors, Arduino platform, and simulation system. The main performance indexes used are the control of a peak consumption level, and the minimum time needed to adjust the distribution of load in the system. The system was able to keep the maximum load at a maximum of 10 kW in less than 10 seconds of response time. The level and response time are controllable parameters.