Title: Cloud-based electricity consumption analysis using neural network

Authors: Nand Kumar; Vilas H. Gaidhane; Ravi Kant Mittal

Addresses: Department of Computer Science, Birla Institute of Technology and Science Pilani, Dubai Campus, Dubai, 345055, UAE ' Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science Pilani, Dubai Campus, Dubai, 345055, UAE ' Department of Computer Science, Birla Institute of Technology and Science Pilani, Pilani Campus, Pilani, 333031, India

Abstract: In recent years, optimisation of the resource usages is necessary to analyse and understand the energy consumption pattern. In the literature, analysis has been carried out using the algorithms, which needs many assumptions, and meeting all the assumptions in practice is a very difficult task. However, there are other methods available to analyse and understand the energy consumption. In this paper, an efficient approach for energy consumption pattern analysis is proposed. It is based on the Levenberg-Marquardt algorithm-based Neural Network (LMNN) and clustering technique. The energy consumption data is collected from the educational institute building using smart system. The various experimentations are carried out on the collected real time database. The experimental results illustrate that the proposed approach is effective and computationally efficient for consumption pattern classification. The performance of the presented approach is found superior to existing clustering approaches.

Keywords: educational institute building; Levenberg-Marquardt algorithm; neural network; classification; confusion matrix; ROC curve.

DOI: 10.1504/IJCAT.2020.103917

International Journal of Computer Applications in Technology, 2020 Vol.62 No.1, pp.45 - 56

Received: 03 Jan 2019
Accepted: 28 Mar 2019
Published online: 02 Dec 2019 *

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