scholarly journals Application of Artificial Neural Networks for Virtual Energy Assessment

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8330
Author(s):  
Amir Mortazavigazar ◽  
Nourehan Wahba ◽  
Paul Newsham ◽  
Maharti Triharta ◽  
Pufan Zheng ◽  
...  
Keyword(s):  
General Information ◽  
Facility Management ◽  
Energy Assessment ◽  
Operational Energy ◽  
Future Consumption ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The University ◽  
Electrical Consumption ◽  
Energy Conservation Measures

A Virtual energy assessment (VEA) refers to the assessment of the energy flow in a building without physical data collection. It has been occasionally conducted before the COVID-19 pandemic to residential and commercial buildings. However, there is no established framework method for conducting this type of energy assessment. The COVID-19 pandemic has catalysed the implementation of remote energy assessments and remote facility management. In this paper, a novel framework for VEA is developed and tested on case study buildings at the University of Melbourne. The proposed method is a hybrid of top-down and bottom-up approaches: gathering the general information of the building and the historical data, in addition to investigating and modelling the electrical consumption with artificial neural network (ANN) with a projection of the future consumption. Through sensitivity analysis, the outdoor temperature was found to be the most sensitive (influential) parameter to electrical consumption. The lockdown of the buildings provided invaluable opportunities to assess electrical baseload with zero occupancies and usage of the building. Furthermore, comparison of the baseload with the consumption projection through ANN modelling accurately quantifies the energy consumption attributed to occupation and operational use, referred to as ‘operational energy’ in this paper. Differentiation and quantification of the baseload and operational energy may aid in energy conservation measures that specifically target to minimise these two distinct energy consumptions.

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>

scholarly journals Artificial neural network based meta-heuristic for performance improvement in physical internet supply chain network

2021 ◽  
Vol 24 (2) ◽  
pp. 1161
Author(s):  
Chouar Abdelsamad ◽  
Tetouani Samir ◽  
Soulhi Aziz ◽  
Elalami Jamila
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Supply Chain ◽  
Logistics System ◽  
Physical Internet ◽  
Feed Forward Neural Networks ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Cost ◽  
The University

<p>Nowadays, reducing total costs while enhancing customer satisfaction is a major task for many supply chain systems. To deal with this issue, the physical internet (PI) paradigm can be represented as a potential replacement for the current logistics system. This paper devoted the cost reduction and lead time improvement in a PI-SCN using a hybrid framework based on an artificial neural network (ANN) and an improved slime mould algorithm (ISMA). To address the performance of the proposed framework, a real-case study in Morocco is considered. The new trainer ISMA’s performance has been investigated in three approximation datasets from the University of California at Irvine (UCI) machine-learning repository regarding nine recent metaheuristics. The experimental results highlight the effectiveness of ISMA according to other meta heuristics for training feed-forward neural networks (FNNs) to converge speed and to avoid local minima.</p>

scholarly journals Very Short-Term Power Forecasting of High Concentrator Photovoltaic Power Facility by Implementing Artificial Neural Network

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3493
Author(s):  
Yaser I. Alamin ◽  
Mensah K. Anaty ◽  
José Domingo Álvarez Hervás ◽  
Khalid Bouziane ◽  
Manuel Pérez García ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Real Data ◽  
Short Term ◽  
Prediction Horizon ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Short Term Prediction ◽  
The University

Concentrator photovoltaic (CPV) is used to obtain cheaper and more stable renewable energy. Methods which predict the energy production of a power system under specific circumstances are highly important to reach the goal of using this system as a part of a bigger one or of making it integrated with the grid. In this paper, the development of a model to predict the energy of a High CPV (HCPV) system using an Artificial Neural Network (ANN) is described. This system is located at the University of Rabat. The performed experiments show a quick prediction with encouraging results for a very short-term prediction horizon, considering the small amount of data available. These conclusions are based on the processes of obtaining the ANN models and detailed discussion of the results, which have been validated using real data.

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

CARBON FOOTPRINT ASSESSMENT AT UNIVERSITI TEKNOLOGI MARA SERI ISKANDAR CAMPUS, MALAYSIA

2017 ◽  
Vol 2 (1) ◽  
pp. 59
Author(s):  
Nor Izana Mohd Shobri ◽  
Wan Noor Anira Hj Wan Ali ◽  
Norizan Mt Akhir ◽  
Siti Rasidah Md Sakip
Keyword(s):  
Total Energy ◽  
Carbon Footprint ◽  
Electrical Power ◽  
Total Carbon ◽  
Green House ◽  
Green House Gas ◽  
Carbon Footprint Assessment ◽  
The University ◽  
Electrical Consumption

The purpose of this study is to assess the carbon footprint emission at UiTM Perak, Seri Iskandar Campus. The assessment focuses on electrical power and transportation usage. Questionnaires were distributed to the staffs and students to survey their transportation usage in the year 2014 while for electrical consumption, the study used total energy consumed in the year 2014. Data was calculating with the formula by Green House Gas Protocol. Total carbon footprint produced by UiTM Perak, Seri Jskandar Campus in the year 2014 is 11842.09 MTC02' The result of the study is hoped to provide strategies for the university to reduce the carbon footprint emission.

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Average Rate ◽  
Mean Square ◽  
Performance Measurements ◽  
Artificial Intelligent ◽  
Average Mean Square Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

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