Development of option c measurement and verification model using hybrid artificial neural network-cross validation technique to quantify saving
| Autor: | Ismail Musirin, Wan Nazirah Wan Md Adnan, Nofri Yenita Dahlan |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Artificial Neural Network
Information Systems and Management Fitness function Artificial neural network Computer science Process (computing) Energy Saving Building energy computer.software_genre Cross-validation Artificial Intelligence Control and Systems Engineering Cross Validation Measurement and Verification Data mining Electrical and Electronic Engineering Measurement & Verification Heating degree day computer Energy (signal processing) Artificial Bee Colony |
| Zdroj: | IAES International Journal of Artificial Intelligence (IJ-AI). 9:25 |
| ISSN: | 2252-8938 2089-4872 |
| DOI: | 10.11591/ijai.v9.i1.pp25-32 |
| Popis: | This paper aims to develop a hybrid artificial neural network for Option C Measurement and Verification model to predict monthly building energy consumption. In this work, baseline energy model development using artificial neural networks embedded with artificial bee colony optimization and cross validation technique for a small dataset were considered. Artificial bee colony optimization with coefficient of correlation fitness function was used in optimizing the neural network training process and selecting the optimal values of initial weights and biases. Working days, class days and cooling degree days were used as input meanwhile monthly electricity consumption as an output of artificial neural network. The results indicated that this hybrid artificial neural network model provided better prediction results compared to the other model. The best model with the highest value of coefficient of correlation was selected as the baseline model hence is used to determine the saving. |
| Databáze: | OpenAIRE |
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