Journal ArticleDOI
A review on time series forecasting techniques for building energy consumption
Reads0
Chats0
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
More filters
Proceedings ArticleDOI
Forecasting of Electric Energy Consumption for Housing Cooperative with a Grid Connected PV System
TL;DR: This paper proposed the simplification of the complexity of the long short-term memory model for the forecasting of the electric energy consumption from a house cooperative in Karlstad, Sweden.
Journal ArticleDOI
Energy time series forecasting-Analytical and empirical assessment of conventional and machine learning models
TL;DR: In this paper, a comprehensive analytical assessment for conventional, machine learning, and deep learning methods that can be utilized to solve various energy time series forecasting (TSF) problems is provided.
Journal ArticleDOI
Identifying temporal properties of building components and indoor environment for building performance assessment
TL;DR: The objective of this study is to uncover the temporal aspects of building properties using data obtained by a wireless sensor network (WSN) and define a set of conditional rules to study ‘ expected’ and ‘unexpected’ impact of the six variables on the indoor air temperature.
Journal ArticleDOI
Domestic hot water consumption prediction models suited for dwellings in central-southern parts of Chile
Alexis Pérez-Fargallo,David Bienvenido-Huertas,Sergio Contreras-Espinoza,Laura Marín-Restrepo +3 more
TL;DR: In this article , the authors analyzed the possibility of using time series models to make future estimations about monthly domestic hot water (DHW) consumption in Chilean housing, and three approaches were applied namely, exponential smoothing, basic structural model (BSM), and state-space model (SSM).
Journal ArticleDOI
Household Energy Consumption Prediction Using the Stationary Wavelet Transform and Transformers
TL;DR: A new technique using machine learning models based on the stationary wavelet transform and transformers to forecast household power consumption in different resolutions is proposed, which achieves superior prediction performance compared to the existing power consumption prediction methods.
References
More filters
Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Journal ArticleDOI
Learning representations by back-propagating errors
TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
Journal ArticleDOI
A logical calculus of the ideas immanent in nervous activity
Warren S. McCulloch,Walter Pitts +1 more
TL;DR: In this article, it is shown that many particular choices among possible neurophysiological assumptions are equivalent, in the sense that for every net behaving under one assumption, there exists another net which behaves under another and gives the same results, although perhaps not in the same time.
Journal ArticleDOI
The perceptron: a probabilistic model for information storage and organization in the brain.
TL;DR: This article will be concerned primarily with the second and third questions, which are still subject to a vast amount of speculation, and where the few relevant facts currently supplied by neurophysiology have not yet been integrated into an acceptable theory.
Book
The perception: a probabilistic model for information storage and organization in the brain
TL;DR: The second and third questions are still subject to a vast amount of speculation, and where the few relevant facts currently supplied by neurophysiology have not yet been integrated into an acceptable theory as mentioned in this paper.
Related Papers (5)
A review of data-driven building energy consumption prediction studies
Kadir Amasyali,Nora El-Gohary +1 more