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Analysis of Novel Random Neural Network Controller for Residential Building Temperature Control

Authors:
Abbas Javed
Hadi Larijani
Ali Ahmadinia
Rohinton Emmanuel

Keywords: random neural network; artificial neural network; building simulation; residential heating system; energy efficient controller

Abstract:
Random neural networks (RNN) have strong generalisation capabilities and are easy to implement on hardware as compared to Artificial Neural Networks (ANN). In this paper, a novel RNN controller is proposed to maintain a comfortable indoor environment in a single zone residential building fitted with radiators for heating. This controller is capable of maintaining a comfortable indoor environment on the basis of a predicted mean vote (PMV)-based set point. The implemented RNN controller is compared with ANN controller for energy consumption, indoor room temperature, and minimum square error. Results show that for same training data and learning algorithm parameters, RNN converges faster and it consumes less energy, results in better comfortable room temperature as compared to ANN controller.

Pages: 63 to 68

Copyright: Copyright (c) IARIA, 2014

Publication date: April 20, 2014

Published in: conference

ISSN: 2308-412X

ISBN: 978-1-61208-332-2

Location: Chamonix, France

Dates: from April 20, 2014 to April 24, 2014