Journal ArticleDOI
A review on time series forecasting techniques for building energy consumption
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TLDR
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.Abstract:
Energy consumption forecasting for buildings has immense value in energy efficiency and sustainability research. Accurate energy forecasting models have numerous implications in planning and energy optimization of buildings and campuses. For new buildings, where past recorded data is unavailable, computer simulation methods are used for energy analysis and forecasting future scenarios. However, for existing buildings with historically recorded time series energy data, statistical and machine learning techniques have proved to be more accurate and quick. This study presents a comprehensive review of the existing machine learning techniques for forecasting time series energy consumption. Although the emphasis is given to a single time series data analysis, the review is not just limited to it since energy data is often co-analyzed with other time series variables like outdoor weather and indoor environmental conditions. The nine most popular forecasting techniques that are based on the machine learning platform are analyzed. An in-depth review and analysis of the ‘hybrid model’, that combines two or more forecasting techniques is also presented. The various combinations of the hybrid model are found to be the most effective in time series energy forecasting for building.read more
Citations
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Pay Attention to Evolution: Time Series Forecasting With Deep Graph-Evolution Learning
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Evaluating forecasting methods in the context of local energy communities
Jonathan Coignard,Maxime Janvier,Vincent Debusschere,Gilles Moreau,Stephanie Chollet,Raphael Caire +5 more
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Development of an Improved Model to Predict Building Thermal Energy Consumption by Utilizing Feature Selection
TL;DR: In this article, the authors proposed a model for predicting the thermal energy consumption of buildings by first extracting major variables through feature selection and deriving significant variables in addition to the collected data and then predicting the energy consumption using a machine learning model.
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