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
Conventional models and artificial intelligence-based models for energy consumption forecasting: A review
TL;DR: The review results show that conventional models are preferred for the yearly energy consumption forecasting in national level and nonlinear regression models can not only explicitly describe the relationship between consumption data and influencing factors but also obtain the lowest average MAPE for long-termEnergy consumption forecasting.
Journal ArticleDOI
Modelling carbon emission intensity: Application of artificial neural network
TL;DR: In this paper, the authors applied an ANN to develop models for forecasting carbon emission intensity for Australia, Brazil, China, India, and USA with negligible forecasting errors, which can serve as tools for international organisations and environmental policymakers to forecast and help in climate change policy decision-making.
Journal ArticleDOI
Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting
TL;DR: Results show that an approach for short-term electricity consumption forecasting based on ensemble learning can help in combining predictions produced by weaker learning methods in order to obtain superior results, and that using an ensemble scheme can achieve very accurate predictions.
Journal ArticleDOI
An Experimental Review on Deep Learning Architectures for Time Series Forecasting
TL;DR: In this article, a comprehensive review of the latest works using deep learning for time series forecasting and an experimental study comparing the performance of the most popular architectures was conducted. And the results show that long short-term memory (LSTM) and convolutional networks (CNN) are the best alternatives, with LSTMs obtaining the most accurate forecasts.
Journal ArticleDOI
Forecasting: theory and practice
TL;DR: In this paper , the authors provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organize, and evaluate forecasts.
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