Journal ArticleDOI
A review on time series forecasting techniques for building energy consumption
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
Predicting residential energy consumption using CNN-LSTM neural networks
Tae Young Kim,Sung-Bae Cho +1 more
TL;DR: This paper proposes a CNN-LSTM neural network that can extract spatial and temporal features to effectively predict the housing energy consumption and achieves almost perfect prediction performance for electric energy consumption that was previously difficult to predict.
Journal ArticleDOI
Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges
TL;DR: An application-oriented review of smart meter data analytics identifies the key application areas as load analysis, load forecasting, and load management and reviews the techniques and methodologies adopted or developed to address each application.
Journal ArticleDOI
Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges
TL;DR: In this paper, the authors conduct an application-oriented review of smart meter data analytics following the three stages of analytics, namely, descriptive, predictive and prescriptive analytics, identifying the key application areas as load analysis, load forecasting, and load management.
Journal ArticleDOI
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.
Journal ArticleDOI
Urban heat island impacts on building energy consumption: A review of approaches and findings
TL;DR: In this paper, the authors reviewed existing literature for improving the understanding of UHI impacts on building energy consumption and found that UHI could result in a median increase of 19.0% in cooling energy consumption, and a median decrease of 18.7% in heating energy consumption.
References
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