Fazli Wahid

Work place: Universiti Tun Hussein Onn, Malaysia

E-mail: wahid_uomian@hotmail.com


Research Interests: Artificial Intelligence, Computational Learning Theory, Neural Networks, Swarm Intelligence, Network Architecture, Data Structures and Algorithms


Fazli Wahid, received BS in Computer Science from University of Malakand, Pakistan in 2006, and MS in Computer Science from SZABIST, Islamabad, Pakistan in 2015. He is currently with University Tun Hussein Onn Malaysia. His areas of interest are Energy Consumption Prediction, Optimization, and Management using Multi-layer Perceptron, Artificial Bee Colony, Ant Colony, Swarm Intelligence, and other Machine Learning Techniques, Artificial Neural Network, Medical Imaging. 

Author Articles
Statistical Features Based Approach (SFBA) for Hourly Energy Consumption Prediction Using Neural Network

By Fazli Wahid Rozaida Ghazali Muhammad Fayaz Abdul Salam Shah

DOI: https://doi.org/10.5815/ijitcs.2017.05.04, Pub. Date: 8 May 2017

In this paper, new statistical features based approach (SFBA) for hourly energy consumption prediction using Multi-Layer Perceptron is presented. The model consists of four stages: data retrieval, data pre-processing, feature extraction and prediction. In the data retrieval stage, historical hourly consumed energy data has been retrieved from the database. During data pre-processing, filters have been applied to make the data more suitable for further processing. In the feature extraction stage, mean, variance, skewness, and kurtosis are extracted. Finally, Multi-Layer Perceptron has been used for prediction. For experimentation with Multi-Layer Perceptron with different training algorithms, a final model of the network was designed in which the scaled conjugate gradient (trainscg) was used as a network training function, tangent sigmoid (Tansig) as a hidden layer transfer function and linear function as an output layer transfer function. For hourly energy consumption prediction, a total of six weeks data of ten residential buildings has been used. To evaluate the performance of the proposed approach, Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), evaluation measurements were applied.

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