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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Proposing energy performance indicators to identify energy-wasting operations on big time-series data
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Prediksi Kunjungan Wisatawan Taman Nasional Gunung Merbabu dengan Time Series Forecasting dan LSTM
TL;DR: The results show that the model applying the lag time method can improve the model's ability to capture patterns on time series data.
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Long sequence time-series forecasting with deep learning: A survey
TL;DR: In this article , the authors provide a comprehensive survey of LSTF studies with deep learning technology and summarize the evolution in terms of a proposed taxonomy based on network structure, and discuss three key problems and corresponding solutions from long dependency modeling, computation cost, and evaluation metrics.
Proceedings ArticleDOI
Uma Proposta de Análise de Biodegradabilidade no Âmbito da Internet das Coisas
TL;DR: In this article, a modelo de equipamento automatizado for determinacao do percentual de biodegradabilidade de materiais, utilizing tecnicas de Internet das coisas e aprendizado de maquina.
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Grasping Inter-Attribute and Temporal Variability in Multivariate Time Series
TL;DR: This work presents an approach that, taking into account the relationships between attributes and their periodicity, reduces the multivariate time series to a collection of symbols, whose distribution is represented by histograms.
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