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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Journal ArticleDOI
Towards developing a systematic knowledge trend for building energy consumption prediction
TL;DR: This paper systematically brings to fore the application areas of building energy consumption prediction (i.e. well-established and emerging), the relationships between these areas and the ways in which authors integrate the current spate of techniques.
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Apparent heat capacity method to investigate heat transfer in a composite phase change material
TL;DR: In this paper, a physical model for heat transfer in a construction material integrating microencapsulated phase change material (PCM) has been studied and analyzed using the apparent heat capacity approach.
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Data-driven modeling of building thermal dynamics: Methodology and state of the art
Zequn Wang,Yuxiang Chen +1 more
TL;DR: Three types of data-driven models, namely transfer-function based, resistor-capacitor based, and artificial-intelligence based, are critically reviewed, including their formulations, interpretability of physical meanings, and prediction accuracy.
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Impact of the COVID-19 Pandemic on Electricity Demand and Load Forecasting
TL;DR: A rolling stochastic Auto Regressive Integrated Moving Average with Exogenous (ARIMAX) model is developed in this paper that aims to improve the forecast performance by capturing the non-smooth demand nature through creating a number of future demand scenarios based on a probabilistic model.
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Big data time series forecasting based on nearest neighbours distributed computing with Spark
TL;DR: A new approach for big data forecasting based on the k-weighted nearest neighbours algorithm for distributed computing under the Apache Spark framework is introduced, leading to the conclusion that the proposed algorithm is highly suitable for bigData environments.
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