Chirag Deb

TL;DR: The various combinations of the hybrid model are found to be the most effective in time series energy forecasting for building and the nine most popular forecasting techniques based on the machine learning platform are analyzed.

TL;DR: In this article, the authors presented a methodology to forecast diurnal cooling load energy consumption for institutional buildings using data driven techniques using Artificial Neural Networks (ANN) and showed that the ANN is able to predict the next day energy use based on five previous days' data with good accuracy.

TL;DR: Comparing the proposed differential evolution algorithm with other evolutionary algorithms show that the proposed model yields higher accuracy for building energy consumption forecasting.

TL;DR: This proposed clustering method based on k-shape algorithm is a relatively novel method to identify shape patterns in time-series data and can detect building energy usage patterns in different time granularity effectively and proves that the forecasting accuracy of SVR model is significantly improved by utilizing the results of the proposed clustered method.

TL;DR: In this article, a robust iterative process is developed utilizing k-means clustering for determining key building variables influencing energy consumption in air-conditioned office buildings, based on assessment of several energy audit reports concerning pre- and post-retrofit data from 56 office buildings.