An Efficient Home Energy Management Scheme Using Cuckoo Search

2017 ◽  
pp. 167-178 ◽  
Author(s):  
Sheraz Aslam ◽  
Rasool Bukhsh ◽  
Adia Khalid ◽  
Nadeem Javaid ◽  
Ibrar Ullah ◽  
...  
Keyword(s):  
Energy Management ◽  
Cuckoo Search ◽  
Management Scheme ◽  
Home Energy Management

Naïve Bayes classifier enabled home energy management scheme for cost-effective end-user comfort

2021 ◽  
Vol 40 (1) ◽  
pp. 403-413
Author(s):  
M. Firdouse Ali Khan ◽  
Ganesh Kumar Chellamani ◽  
Premanand Venkatesh Chandramani
Keyword(s):  
Energy Management ◽  
Time Window ◽  
Cost Effective ◽  
Pattern Generation ◽  
Home Appliances ◽  
Management Scheme ◽  
Home Energy Management ◽  
Maximum Comfort ◽  
The Cost ◽  
User Comfort

Under demand response enabled demand-side management, the home energy management (HEM) schemes schedule appliances for balancing both energy and demand within a residence. This scheme enables the user to achieve either a minimum electricity bill (EB) or maximum comfort. There is always the added burden on a HEM scheme to obtain the least possible EB with comfort. However, if a time window that contains comfortable time slots of the day for an appliance operation, is identified, and if the cost-effective schedule-pattern gets generated from these windows autonomously, then the burden can be reduced. Therefore, this paper proposes a two-level method that can assist the HEM scheme by generating a cost-effective schedule-pattern for scheduling home appliances. The first level uses a classifier to identify the comfortable time window from past ON and OFF events. The second level uses pattern generation algorithms to generate a cost-effective schedule-pattern from the identified window. The generated cost-effective schedule-pattern is applied to a HEM scheme as input to demonstrate the proposed two-level approach. The simulation results exhibit that the proposed approach helps the HEM scheme to schedule home appliances cost-effectively with a satisfactory user-comfort between 90% and 100%.

scholarly journals Development of Home Energy Management Scheme for a Smart Grid Community

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4288 ◽  
Author(s):  
Md Mamun Ur Rashid ◽  
Fabrizio Granelli ◽  
Md. Alamgir Hossain ◽  
Md. Shafiul Alam ◽  
Fahad Saleh Al-Ismail ◽  
...  
Keyword(s):  
Smart Grid ◽  
Energy Management ◽  
Renewable Energy Sources ◽  
Operating Time ◽  
Power Sharing ◽  
Electricity Cost ◽  
Home Appliances ◽  
Optimal Power ◽  
Management Scheme ◽  
Home Energy Management

The steady increase in energy demand for residential consumers requires an efficient energy management scheme. Utility organizations encourage household applicants to engage in residential energy management (REM) system. The utility’s primary goal is to reduce system peak load demand while consumer intends to reduce electricity bills. The benefits of REM can be enhanced with renewable energy sources (RESs), backup battery storage system (BBSS), and optimal power-sharing strategies. This paper aims to reduce energy usages and monetary cost for smart grid communities with an efficient home energy management scheme (HEMS). Normally, the residential consumer deals with numerous smart home appliances that have various operating time priorities depending on consumer preferences. In this paper, a cost-efficient power-sharing technique is developed which works based on priorities of appliances’ operating time. The home appliances are sorted on priority basis and the BBSS are charged and discharged based on the energy availability within the smart grid communities and real time energy pricing. The benefits of optimal power-sharing techniques with the RESs and BBSS are analyzed by taking three different scenarios which are simulated by C++ software package. Extensive case studies are carried out to validate the effectiveness of the proposed energy management scheme. It is demonstrated that the proposed method can save energy and reduce electricity cost up to 35% and 45% compared to the existing methods.

Optimal configuration and energy management scheme of an isolated micro-grid using Cuckoo search optimization algorithm

2019 ◽  
Vol 356 (8) ◽  
pp. 4191-4214 ◽  
Author(s):  
Yazan M. Alsmadi ◽  
Alaa M. Abdel-hamed ◽  
Abo Eleyoun Ellissy ◽  
Amged S. El-Wakeel ◽  
Almoataz Y. Abdelaziz ◽  
...  
Keyword(s):  
Energy Management ◽  
Optimization Algorithm ◽  
Cuckoo Search ◽  
Optimal Configuration ◽  
Management Scheme ◽  
Search Optimization ◽  
Cuckoo Search Optimization ◽  
Micro Grid

Supervised electricity tariff prediction using random forest validated through user comfort and constraint for a home energy management scheme

2021 ◽  
Vol 40 (1) ◽  
pp. 745-757
Author(s):  
Ganesh Kumar Chellamani ◽  
M. Firdouse Ali Khan ◽  
Premanand Venkatesh Chandramani
Keyword(s):  
Random Forest ◽  
Energy Management ◽  
Performance Metrics ◽  
Support Vector ◽  
End User ◽  
Short Term ◽  
Management Scheme ◽  
Home Energy Management ◽  
Electricity Tariff ◽  
User Comfort

Day-ahead electricity tariff prediction is advantageous for both consumers and utilities. This article discusses the home energy management (HEM) scheme consisting of an electricity tariff predictor and appliance scheduler. The random forest (RF) technique predicts a short-term electricity tariff for the next 24 hours using the past three months of electricity tariff information. This predictor provides the tariff information to schedule the appliances at the most preferred time slot of a consumer with minimum electricity tariff, aiming high consumer comfort and low electricity bill for consumers. The proposed approach allows a user to be aware of their demand and their comfort. The proposed approach makes use of present-day (D) tariff and immediate previous 30 days (D-1, D-2, ...  , D-30) of tariff information for training achieves minimum error values for next day electricity tariff prediction. The simulation results demonstrate the benefits of the RF approach for tariff prediction by comparing it with the support vector machine (SVM) and decision tree (DT) predicted tariffs against the actual tariff, provided by the utility day-ahead. The outcomes indicate that the RF produces the best results compared to SVM and DT predictions for performance metrics and end-user comfort.

Home Energy Management System

2012 ◽  
Vol 132 (10) ◽  
pp. 695-697 ◽  
Author(s):  
Hideki HAYASHI ◽  
Yukitoki TSUKAMOTO ◽  
Shouji MOCHIZUKI
Keyword(s):  
Energy Management ◽  
Management System ◽  
Energy Management System ◽  
Home Energy Management ◽  
Home Energy Management System
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