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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ARIMA Models in Electrical Load Forecasting and Their Robustness to Noise
TL;DR: In this article, the authors address the problem of insufficient knowledge on the impact of noise on the auto-regressive integrated moving average (ARIMA) model identification and propose a simulation-based solution to the analysis of the tolerance to noise of ARIMA models in electrical load forecasting.
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Measuring the Heat Transfer Coefficient (HTC) in buildings : a stakeholder’s survey
TL;DR: The results reveal that the stakeholders are highly interested in measuring the HTC on-site and elaborate on their perspective on the time to conduct the measurement, the cost of the setup, the measurement duration and the acceptable error.
Journal ArticleDOI
Load Forecasting Techniques and Their Applications in Smart Grids
TL;DR: In this paper , the authors conducted a systematic review of state-of-the-art load forecasting techniques, including traditional techniques, clustering-based techniques, AI-based and time series-based methods, and provided an analysis of their performance and results.
Journal ArticleDOI
Pro-Environmental Behaviour in the European Union Countries
TL;DR: In this paper, the authors used a synthetic measure developed using the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) benchmark method to assess pro-environmental behaviour (PEB) in European Union countries in 2009 and 2019.
Journal ArticleDOI
Things2People interaction toward energy savings in shared spaces Using BIM
TL;DR: An approach to create personalized local energy consumption predictions in a building using past sensor data, correlated with external conditions to create local context predictions is presented, which allows Things2People interaction to improve energy savings in these shared spaces.
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