Killer whale-backpropagation (KW-BP) algorithm for accuracy improvement of neural network forecasting models on energy-efficient data

Green technology building is not newly introduced to the world nor Malaysia, but it is rarely practiced globally and now it has promoted noteworthy due to destructions caused by human hands towards the nature. Now people started to realize that the world is polluted by many hazardous substances. The...

Full description

Bibliographic Details
Published in:IAES International Journal of Artificial Intelligence (IJ-AI)
Main Authors: Saadi Bin Ahmad Kamaruddin, Nor Azura Md Ghani, Hazrita Ab Rahim, Ismail Musirin
Format: Article in Journal/Newspaper
Language:unknown
Published: 2019
Subjects:
Backpropagation algorithm
Energy-efficient
Forecasting
Green technology campus
Killer whale
Killer Whale
Online Access:https://zenodo.org/record/6935538
https://doi.org/10.11591/ijai.v8.i3.pp270-277
Description
Summary:Green technology building is not newly introduced to the world nor Malaysia, but it is rarely practiced globally and now it has promoted noteworthy due to destructions caused by human hands towards the nature. Now people started to realize that the world is polluted by many hazardous substances. Therefore, Help University came up with the effort of preserving the nature through a new Green Technology campus, which has been fully operated since year 2017. In this research, neural network forecasting models on energy-efficient data of Help University, Subang 2 green technology campus at Subang Bistari, Selangor has been done with respect to value-for-money (VFM) attribute. Previously there were no similar research done on energy-efficient data of Help University, Subang 2 campus. The significant factors with respect to energy or electricity saved (MW/hr) in the year 2017 variable were studied as recorded by Building Automation and Control System (BAS) of Help University Subang 2 campus. Using multiple linear regression (stepwise method), the significant predictor towards energy saved (MW/hr) was Building Energy Index (BEI) (kWh/m2/year) based p-value<α=0.05. A mathematical model was developed. Moreover, the proposed neural network forecasting model using Killer Whale-Backpropagation Algorithm (KWBP) were found to better than existing conventional techniques to forecast BEI data. This research is expected to specifically assist maintenance department of Help University, Subang 2 campus towards load forecasting for power saving planning in years to come.

Similar Items