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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Journal ArticleDOI
Hybrid Deep Neural Network Model for Multi-Step Energy Prediction of Prosumers
TL;DR: A 24-steps-ahead energy prediction model that integrates clustering and multilayer perceptron classification models used to detect the classes of energy profiles and multilevel perceptron regression models used for fine-tune the energy prediction, considering the energy data streams is proposed.
Proceedings ArticleDOI
Wavelet-Extreme Learning i-Machine for New Zealand Smart Meter Data
TL;DR: In this paper, a wavelet decomposition based artificial neural network trained with an extreme learning machine is proposed for long-term load forecasting based on continuous learning, which has been undertaken in pursuit of creating a software tool that can predict the impact of increased renewable distributed sources on load profiles.
Journal ArticleDOI
Bridging the gap between mechanistic biological models and machine learning surrogates
Ioana Gherman,Zahraa Said Abdallah,Wei-Zhi Pang,Thomas E. Gorochowski,Claire S. Grierson,Lucia Marucci +5 more
TL;DR: In this paper , the authors provide an overview of the relevant literature, both from an applicability and a theoretical perspective, and present a perspective on how these approaches can be applied to models representing biological processes with potential industrial applications.
Book ChapterDOI
Deep Learning Models for Time Series Forecasting of Indoor Temperature and Energy Consumption in a Cold Room
TL;DR: Four deep neural network architectures derived from the LSTM architecture were studied, adapted and compared, and their validation was carried out using experimental data collected in a cold room in order to assess their performance in predicting demand response.
Journal ArticleDOI
An Efficient Decision-Making Approach for Short Term Indoor Room Temperature Forecasting in Smart Environment: Evidence from India
TL;DR: “Smart cities” start with “Smart Buildings” that improve the quality of urban services while ensuring sustainability, according to the current scenario in India.
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