Paper Title
Predication Model optimized by an Intelligent Algorithm for Energy Consumption

The steadily rising costs of energy is naught but wrought by man’s own doing; our over-reliance on precious, unrenewable fuel to power everything we use has led to a slow bleeding of a very finite resource pool. This paper proposes one such measure that utilizes the nascent Internet of Things framework in a lightweight system that is capable of manipulating and monitoring energy usage at a localized level, thus providing vital information that would aid in an optimal configuration of a building-wide energy dissemination system. In this work, an improved hybrid model based on the Autoregressive Integrated Moving Average (ARIMA) and Gaussian Sum Particle Filtering (GSPT) is proposed to predict energy usage. Observations with similar features can be grouped in the same cluster using clustering-based algorithm which is inspired by Imperialist Competitive Algorithm (ICA) and k-nearest neighbor (kNN) classification, which is referred to as ICA-kNN, and it is commonly used to reach optimum clustering N objects into K clusters. ICA-kNN algorithm is used to develop the hybrid model and optimize the ARIMA model parameters. The ICA process is not only able to immensely cut the computation load, but also to enhance the model performance. By combining the ARIMA and an optimized ICA-kNN algorithm, the GSPF accomplishes the best performance. Moreover, proposed model constructs an accurate tool to predict the global energy consumption issues which has not been encountered effectively so far. Keywords: Energy consumption, Prediction, ARIMA, Intelligent algorithm.