scholarly journals A Novel Robust Smart Energy Management and Demand Reduction for Smart Homes Based on Internet of Energy

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4756
Author(s):  
Bilal Naji Alhasnawi ◽  
Basil H. Jasim ◽  
Zain-Aldeen S. A. Rahman ◽  
Pierluigi Siano
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Optimization Algorithm ◽  
Artificial Bee Colony ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Bee Colony ◽  
Home Energy Management ◽  
Grid Topology ◽  
Smart Energy Management

In residential energy management (REM), Time of Use (ToU) of devices scheduling based on user-defined preferences is an essential task performed by the home energy management controller. This paper devised a robust REM technique capable of monitoring and controlling residential loads within a smart home. In this paper, a new distributed multi-agent framework based on the cloud layer computing architecture is developed for real-time microgrid economic dispatch and monitoring. In this paper the grey wolf optimizer (GWO), artificial bee colony (ABC) optimization algorithm-based Time of Use (ToU) pricing model is proposed to define the rates for shoulder-peak and on-peak hours. The results illustrate the effectiveness of the proposed the grey wolf optimizer (GWO), artificial bee colony (ABC) optimization algorithm based ToU pricing scheme. A Raspberry Pi3 based model of a well-known test grid topology is modified to support real-time communication with open-source IoE platform Node-Red used for cloud computing. Two levels communication system connects microgrid system, implemented in Raspberry Pi3, to cloud server. The local communication level utilizes IP/TCP and MQTT is used as a protocol for global communication level. The results demonstrate and validate the effectiveness of the proposed technique, as well as the capability to track the changes of load with the interactions in real-time and the fast convergence rate.

An Application of Grey Wolf Optimizer for Commodity Price Forecasting

2015 ◽  
Vol 785 ◽  
pp. 473-478
Author(s):  
Zuriani Mustaffa ◽  
Mohd Herwan Sulaiman ◽  
Yuhanis Yusof
Keyword(s):  
Prediction Accuracy ◽  
Artificial Bee Colony ◽  
Commodity Price ◽  
Percentage Error ◽  
Grey Wolf Optimizer ◽  
Price Forecasting ◽  
Grey Wolf ◽  
Gasoline Price ◽  
Bee Colony ◽  
De Algorithm

Over the recent decades, there are many nature inspired optimization algorithms have been introduced. In this study, a newly algorithm namely Grey Wolf Optimizer (GWO) is employed for gasoline price forecasting. The performance of GWO is compared against the results produced by Artificial Bee Colony (ABC) algorithm and Differential Evolution (DE) algorithm. Measured based on Mean Absolute Percentage Error (MAPE) and prediction accuracy, the GWO is proven to produce significantly better results as compared to the identified algorithms.

scholarly journals artificial bee colony optimization algorithm for adaptive power scheduling in an isolated system

2021 ◽  
Vol 21 (2) ◽  
pp. 1168
Author(s):  
Vijo M. Joy ◽  
S. Krishnakumar
Keyword(s):  
Neural Network ◽  
Energy Management ◽  
Optimization Algorithm ◽  
Artificial Bee Colony ◽  
Search Algorithm ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Power Scheduling ◽  
Marquardt Algorithm ◽  
Bee Colony Optimization

<p>The objective of this work is to solve the power scheduling problems for efficient energy management by assigning the optimal values. Artificial neural networks are used widely in the field of energy management and load scheduling. The backpropagation technique is used for the feed-forward neural network training and the levenberg-marquardt algorithm is used to minimize the errors. The slow speed of convergence and getting stuck in local minima are some negatives of backpropagation in complex computation. To overcome these drawbacks an innovative meta-heuristic search algorithm called modified artificial bee colony optimization algorithm is used. A hybrid neural network is introduced in this work. The simulation result shows that the efficiency of the system is improved when hybrid optimization is used. With this method, the system achieves an optimal accuracy of 99.23%.</p>

Dynamic characterization of a master–slave robotic manipulator using a hybrid grey wolf–whale optimization algorithm

2021 ◽  
pp. 107754632110034
Author(s):  
Ololade O Obadina ◽  
Mohamed A Thaha ◽  
Kaspar Althoefer ◽  
Mohammad H Shaheed
Keyword(s):  
Optimization Algorithm ◽  
Hybrid Algorithm ◽  
Robot Manipulator ◽  
Moving Average ◽  
Identification Problem ◽  
Whale Optimization Algorithm ◽  
Grey Wolf Optimizer ◽  
Parametric Modelling ◽  
Grey Wolf ◽  
Whale Optimization

This article presents a novel hybrid algorithm based on the grey-wolf optimizer and whale optimization algorithm, referred here as grey-wolf optimizer–whale optimization algorithm, for the dynamic parametric modelling of a four degree-of-freedom master–slave robot manipulator system. The first part of this work consists of testing the feasibility of the grey-wolf optimizer–whale optimization algorithm by comparing its performance with a grey-wolf optimizer, whale optimization algorithm and particle swarm optimization using 10 benchmark functions. The grey-wolf optimizer–whale optimization algorithm is then used for the model identification of an experimental master–slave robot manipulator system using the autoregressive moving average with exogenous inputs model structure. Obtained results demonstrate that the hybrid algorithm is effective and can be a suitable substitute to solve the parameter identification problem of robot models.

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