Artificial Neural Network for Non-Intrusive Electrical Energy Monitoring System

2017 ◽  
Vol 6 (1) ◽  
pp. 124
Author(s):  
Khairell Khazin Kaman ◽  
Mahdi Faramarzi ◽  
Sallehuddin Ibrahim ◽  
Mohd Amri Md Yunus
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Electrical Energy ◽  
Energy Monitoring ◽  
Training Time ◽  
Energy Meter ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Neurons ◽  
Electrical Consumption

<p> This paper discusses non-intrusive electrical energy monitoring (NIEM) system in an effort to minimize electrical energy wastages. To realize the system, an energy meter is used to measure the electrical consumption by electrical appliances. The obtained data were analyzed using a method called multilayer perceptron (MLP) technique of artificial neural network (ANN). The event detection was implemented to identify the type of loads and the power consumption of the load which were identified as fan and lamp. The switching ON and OFF output events of the loads were inputted to MLP in order to test the capability of MLP in classifying the type of loads. The data were divided to 70% for training, 15% for testing, and 15% for validation. The output of the MLP is either ‘1’ for fan or ‘0’ for lamp. In conclusion, MLP with five hidden neurons results obtained the lowest average training time with 2.699 seconds, a small number of epochs with 62 iterations, a min square error of 7.3872×10-5, and a high regression coefficient of 0.99050.</p>

Tool Condition Monitoring Using Artificial Neural Network Models

2022 ◽  
pp. 400-426
Author(s):  
Srinivasa P. Pai ◽  
Nagabhushana T. N.
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Condition Monitoring ◽  
Medium Carbon Steel ◽  
Tool Condition Monitoring ◽  
Training Time ◽  
Tool Condition ◽  
Cell Structures ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Tool wear is a major factor that affects the productivity of any machining operation and needs to be controlled for achieving automation. It affects the surface finish, tolerances, dimensions of the workpiece, increases machine down time, and sometimes performance of machine tool and personnel are affected. This chapter deals with the application of artificial neural network (ANN) models for tool condition monitoring (TCM) in milling operations. The data required for training and testing the models studied and developed are from live experiments conducted in a machine shop on a widely used steel, medium carbon steel (En 8) using uncoated carbide inserts. Acoustic emission data and surface roughness data has been used in model development. The goal is for developing an optimal ANN model, in terms of compact architecture, least training time, and its ability to generalize well on unseen (test) data. Growing cell structures (GCS) network has been found to achieve these requirements.

Towards Developing the Piece-Wise Linear Neural Network Algorithm for Rule Extraction

2020 ◽  
pp. 1632-1649
Author(s):  
Veronica Chan ◽  
Christine W. Chan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Equations ◽  
Rule Extraction ◽  
Ann Model ◽  
Factors Affecting ◽  
Artificial Neural ◽  
Linear Neural Network ◽  
Artificial Neural Network Ann ◽  
Hidden Neurons

This paper discusses development and application of a decomposition neural network rule extraction algorithm for nonlinear regression problems. The algorithm is called the piece-wise linear artificial neural network or PWL-ANN algorithm. The objective of the algorithm is to “open up” the black box of a neural network model so that rules in the form of linear equations are generated by approximating the sigmoid activation functions of the hidden neurons in an artificial neural network (ANN). The preliminary results showed that the algorithm gives high fidelity and satisfactory results on sixteen of the nineteen tested datasets. By analyzing the values of R2 given by the PWL approximation on the hidden neurons and the overall output, it is evident that in addition to accurate approximation of each individual node of a given ANN model, there are more factors affecting the fidelity of the PWL-ANN algorithm Nevertheless, the algorithm shows promising potential for domains when better understanding about the problem is needed.

Development of Hybrid Artificial Neural Network for Quantifying Energy Saving using Measurement and Verification

2017 ◽  
Vol 8 (1) ◽  
pp. 137
Author(s):  
Wan n Nazirah Wan Md Adna ◽  
Nofri Yenita Dahlan ◽  
Ismail Musirin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Use ◽  
Model Development ◽  
Operating Time ◽  
Electrical Energy ◽  
Measurement And Verification ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hybrid Artificial Neural Network

This paper presents a Hybrid Artificial Neural Network (HANN) for chiller system Measurement and Verification (M&amp;V) model development. In this work, hybridization of Evolutionary Programming (EP) and Artificial Neural Network (ANN) are considered in modeling the baseline electrical energy consumption for a chiller system hence quantifying saving. EP with coefficient of correlation (R) objective function is used in optimizing the neural network training process and selecting the optimal values of ANN initial weights and biases. Three inputs that are affecting energy use of the chiller system are selected; 1) operating time, 2) refrigerant tonnage and 3) differential temperature. The output is hourly energy use of building air-conditioning system. The HANN model is simulated with 16 different structures and the results reveal that all HANN structures produce higher prediction performance with R is above 0.977. The best structure with the highest value of R is selected as the baseline model hence is used to determine the saving. The avoided energy calculated from this model is 132944.59 kWh that contributes to 1.38% of saving percentage.

scholarly journals Classification of EMG signal for multiple hand gestures based on neural network

2020 ◽  
Vol 17 (1) ◽  
pp. 256 ◽  
Author(s):  
Mohd Azlan Abu ◽  
Syazwani Rosleesham ◽  
Mohd Zubir Suboh ◽  
Mohd Syazwan Md Yid ◽  
Zainudin Kornain ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Invasive Technique ◽  
Hand Gestures ◽  
Emg Signal ◽  
The Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Neurons

<span>This paper presents the classification of EMG signal for multiple hand gestures based on neural network. In this study, the Electromyography is used to measure the muscle cell’s electrical activities which is commonly represented in a function time. Every muscle has their own signals, which was produced in every movement. Surface electromyography (sEMG) is used as a non-invasive technique for acquiring the EMG signal. The development of sensors’ detection and measuring the EMG have been improved and have become more precise while maintaining a small size. In this paper, the main objective is to identify the hand gestures based on: (1) Cylindrical Grasp, (2) Supination (Twist Left), (3) Pronation (Twist Right), (4) Resting Hand and (5) Open Hand that are predefined by using Arduino IDE, CoolTerm software and Microsoft Excel before using artificial neural network for classifying purposes in MATLAB. Finally, the extraction of the EMG patterns for each movement went through features extraction of the signals which is used to train the classifier in MATLAB to classify signals in the neural network. The features extracted are using mean absolute value (MAV), median, waveform length (WL) and root mean square (RMS). The Artificial Neural Network (ANN) produced accuracy of 80% for training and testing for 10 hidden neurons layer.</span>

scholarly journals Application of artificial neural network in sizing a stand-alone photovoltaic system: a review

2020 ◽  
Vol 11 (1) ◽  
pp. 342 ◽  
Author(s):  
Ahmad Fateh Mohamad Nor ◽  
Suriana Salimin ◽  
Mohd Noor Abdullah ◽  
Muhammad Nafis Ismail
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Electrical Energy ◽  
Design Tool ◽  
Photovoltaic System ◽  
Pv System ◽  
System A ◽  
Load Demand ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<span>Artificial Neural Network (ANN) techniques are becoming useful in the current era due to the vast development of the current computer technologies. ANN has been used in various fields especially in the field of science and technology. One of the advantage that makes ANN so interesting is the ANN’s ability to learn the input and output relationship even though the relationship is non-linear. In addition, ANN is also useful for modelling, optimization, prediction, forecasting, and controlling systems. The main objective of this paper is to present a review of the ANN techniques for sizing a stand-alone photovoltaic (PV) system. The review in this paper shows the potential of ANN as a design tool for a stand-alone PV. In addition, ANN is very useful to improve the sizing process of the stand-alone PV system. The sizing process is of paramount importance to a stand-alone PV system in order to make sure the system can generate ample electrical energy to supply the load demand.</span>

Tool Condition Monitoring Using Artificial Neural Network Models

2020 ◽  
pp. 550-576
Author(s):  
Srinivasa P. Pai ◽  
Nagabhushana T. N.
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Condition Monitoring ◽  
Medium Carbon Steel ◽  
Tool Condition Monitoring ◽  
Training Time ◽  
Tool Condition ◽  
Cell Structures ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Tool wear is a major factor that affects the productivity of any machining operation and needs to be controlled for achieving automation. It affects the surface finish, tolerances, dimensions of the workpiece, increases machine down time, and sometimes performance of machine tool and personnel are affected. This chapter deals with the application of artificial neural network (ANN) models for tool condition monitoring (TCM) in milling operations. The data required for training and testing the models studied and developed are from live experiments conducted in a machine shop on a widely used steel, medium carbon steel (En 8) using uncoated carbide inserts. Acoustic emission data and surface roughness data has been used in model development. The goal is for developing an optimal ANN model, in terms of compact architecture, least training time, and its ability to generalize well on unseen (test) data. Growing cell structures (GCS) network has been found to achieve these requirements.

scholarly journals Combinatory Finite Element and Artificial Neural Network Model for Predicting Performance of Thermoelectric Generator

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2216 ◽  
Author(s):  
Ravi Kishore ◽  
Roop Mahajan ◽  
Shashank Priya
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Electrical Energy ◽  
Optimization Techniques ◽  
Operating Conditions ◽  
Ann Model ◽  
Predicting Performance ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Thermoelectric generators (TEGs) are rapidly becoming the mainstream technology for converting thermal energy into electrical energy. The rise in the continuous deployment of TEGs is related to advancements in materials, figure of merit, and methods for module manufacturing. However, rapid optimization techniques for TEGs have not kept pace with these advancements, which presents a challenge regarding tailoring the device architecture for varying operating conditions. Here, we address this challenge by providing artificial neural network (ANN) models that can predict TEG performance on demand. Out of the several ANN models considered for TEGs, the most efficient one consists of two hidden layers with six neurons in each layer. The model predicted TEG power with an accuracy of ±0.1 W, and TEG efficiency with an accuracy of ±0.2%. The trained ANN model required only 26.4 ms per data point for predicting TEG performance against the 6.0 minutes needed for the traditional numerical simulations.

Towards Developing the Piece-Wise Linear Neural Network Algorithm for Rule Extraction

2017 ◽  
Vol 11 (2) ◽  
pp. 57-73 ◽  
Author(s):  
Veronica Chan ◽  
Christine Chan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Equations ◽  
Rule Extraction ◽  
Ann Model ◽  
Factors Affecting ◽  
Artificial Neural ◽  
Linear Neural Network ◽  
Artificial Neural Network Ann ◽  
Hidden Neurons

This paper discusses development and application of a decomposition neural network rule extraction algorithm for nonlinear regression problems. The algorithm is called the piece-wise linear artificial neural network or PWL-ANN algorithm. The objective of the algorithm is to “open up” the black box of a neural network model so that rules in the form of linear equations are generated by approximating the sigmoid activation functions of the hidden neurons in an artificial neural network (ANN). The preliminary results showed that the algorithm gives high fidelity and satisfactory results on sixteen of the nineteen tested datasets. By analyzing the values of R2 given by the PWL approximation on the hidden neurons and the overall output, it is evident that in addition to accurate approximation of each individual node of a given ANN model, there are more factors affecting the fidelity of the PWL-ANN algorithm Nevertheless, the algorithm shows promising potential for domains when better understanding about the problem is needed.

scholarly journals A Novel User Classification Method for Femtocell Network by Using Affinity Propagation Algorithm and Artificial Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Afaz Uddin Ahmed ◽  
Mohammad Tariqul Islam ◽  
Mahamod Ismail ◽  
Salehin Kibria ◽  
Haslina Arshad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Affinity Propagation ◽  
Training Time ◽  
User Classification ◽  
Training Samples ◽  
Femtocell Network ◽  
Artificial Neural ◽  
The Difference ◽  
Artificial Neural Network Ann

An artificial neural network (ANN) and affinity propagation (AP) algorithm based user categorization technique is presented. The proposed algorithm is designed for closed access femtocell network. ANN is used for user classification process and AP algorithm is used to optimize the ANN training process. AP selects the best possible training samples for faster ANN training cycle. The users are distinguished by using the difference of received signal strength in a multielement femtocell device. A previously developed directive microstrip antenna is used to configure the femtocell device. Simulation results show that, for a particular house pattern, the categorization technique without AP algorithm takes 5 indoor users and 10 outdoor users to attain an error-free operation. While integrating AP algorithm with ANN, the system takes 60% less training samples reducing the training time up to 50%. This procedure makes the femtocell more effective for closed access operation.

Sign in / Sign up

Export Citation Format

Share Document