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
More filters
Proceedings ArticleDOI
Inadequacy of Standard Algorithms and Metrics for Short-Term Load Forecasts in Low-Voltage Grids
TL;DR: This paper discusses the performance of four forecasting methods, including a novel algorithm based on Markov chains and investigates different approaches to assess the forecast performance on volatile loads and notably compare the statistical properties of the predictions.
Journal ArticleDOI
Proactively Monitoring Large Project Portfolios
TL;DR: The discipline of project management has evolved over the years, yet projects still run into trouble, failing entirely, running late, or not delivering expected benefits as discussed by the authors, which is a concern of project managers.
Journal ArticleDOI
Water-energy benchmarking and predictive modeling in multi-family residential and non-residential buildings
TL;DR: The clustering analysis performed in this work revealed that water and energy consumption patterns of heterogeneous buildings are not exclusively characterized by general building characteristics, which highlights the value of data-driven modeling for revealing meaningful insights into usage patterns.
Journal ArticleDOI
Forecasting household electricity demand with hybrid machine learning-based methods: Effects of residents' psychological preferences and calendar variables
TL;DR: Wang et al. as discussed by the authors proposed a two-layer feature selection method with the combination of random forest and autocorrelation analysis to select the most relevant features, which simultaneously considered correlations and importance of input lagged variables as well as exogenous variables.
Journal ArticleDOI
WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series Tasks
TL;DR: WOODS: eight challenging open-source time series benchmarks covering a diverse range of data modalities, such as videos, brain recordings, and sensor signals is presented, underscoring the new challenges posed by time series tasks.
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