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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Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges
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Modeling and forecasting building energy consumption: A review of data-driven techniques
TL;DR: A review of studies developing data-driven models for building scale applications with a focus on the input data characteristics and data pre-processing methods, the building typologies considered, the targeted energy end-uses and forecasting horizons, and accuracy assessment.
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Urban heat island impacts on building energy consumption: A review of approaches and findings
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References
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Techniques of applying wavelet transform into combined model for short-term load forecasting
TL;DR: Techniques of applying wavelet transform into combined model for short-term load forecasting are presented and it is found that even the model works well for certain load components, it will be not suitable for other components because it cannot consider every factor.
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Journal ArticleDOI
Techniques of applying wavelet de-noising into a combined model for short-term load forecasting
TL;DR: In this article, a wavelet de-noising in a combined model that is a hybrid of the seasonal autoregressive integrated moving average model (SARIMA) and neural networks is proposed for short-term load forecasting.
Book ChapterDOI
Time-series prediction: Application to the short-term electric energy demand
Alicia Troncoso Lora,Jesús Manuel Riquelme Santos,José C. Riquelme,Antonio Gómez Expósito,José Luis Martínez Ramos +4 more
TL;DR: In this paper, a time-series prediction method based on the kNN technique is proposed for the 24-hour load forecasting problem in the Spanish transmission system, where the parameters are estimated by solving a least square problem.
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