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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Predicting residential energy consumption using CNN-LSTM neural networks
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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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Short-Term Load Forecasting Using Random Forests.
TL;DR: In this paper, the authors proposed using a random forest model for short-term electricity load forecasting, which is an ensemble learning method that generates many regression trees (CART) and aggregates their results.
Journal ArticleDOI
Forecasting Electricity Consumption with Neural Networks and Support Vector Regression
TL;DR: In this paper, the authors used the state of the art computation methods to forecast the electricity consumption of Turkey and compared the forecast results with real consumption values to measure the performance of the methods.
Journal ArticleDOI
Application of a hybrid quantized Elman neural network in short-term load forecasting
TL;DR: The results indicate that the forecasting method based on HQENN has an acceptable high accuracy and the genetic algorithm is introduced to obtain the optimal or suboptimal structure of the HQENN model.
Journal ArticleDOI
Improved estimation of electricity demand function by integration of fuzzy system and data mining approach
TL;DR: An integrated fuzzy system and data mining approach for estimation of electricity demand function in Iran and the prescribed approach may be an ideal substitute for fuzzy regression is presented.
Journal ArticleDOI
A Review of Wireless-Sensor-Network-Enabled Building Energy Management Systems
TL;DR: The state-of-the-art in building energy management systems is surveyed and a generic architecture is proposed after which a detailed taxonomy of existing documented systems is presented.
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