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
Strain prediction for historical timber buildings with a hybrid Prophet-XGBoost model
Jun Wang,Xiaoying Du,Xin Qi +2 more
TL;DR: In this paper , an integrated model based on combining the Prophet and Extreme Gradient Boosting (XGBoost) methods was proposed to exclude environmental impacts in future structural state assessments by constructing a new optimized weighting method.
Proceedings ArticleDOI
Support vector machines for classification of electrical resistance values within a VSI
TL;DR: In this work, the performance of SVM for estimation of electrical resistance values in a Voltage Source Inverter (VSI) was experimentally proved and the potentialities of this Machine Learning tool for the nonintrusive monitoring of Electrical resistance in a VSI were confirmed.
Journal ArticleDOI
Winds of Change: How Up-To-Date Forecasting Methods Could Help Change Brazilian Wind Energy Policy and Save Billions of US$
TL;DR: In this article, the authors proposed a revaluation of the Brazilian wind energy policy framework and the energy auction requirements, through the adoption of international standards by Brazilian policy-makers, reducing the wind time sampling necessary to implement wind farms.
Journal ArticleDOI
Accurate electricity consumption prediction using enhanced long short-term memory
TL;DR: In this paper , a novel deep learning model named enhanced long short-term memory (E-LSTM) is proposed to accurately predict electricity consumption in advance as this model will accurately predict the electricity consumption by adjusting the number of hidden layers and optimizing the hyper-parameters.
Journal ArticleDOI
Design and development of a forecasting tool for the identification of new target markets by open time-series data and deep learning methods
TL;DR: In this paper , a multidisciplinary framework, called MULTIFOR, is proposed to synthesize and deliver profound knowledge about the macroeconomic environment and the attractiveness of new target markets.
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