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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.

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Book ChapterDOI

Using Deep Neural Networks for Forecasting Cell Congestion on LTE Networks: A Simple Approach

TL;DR: A dataset of collected downlink throughput samples from one cell in an area where cell congestion usually occurs and a Deep Neural Network approach are used to perform short-term cell load forecasting and results indicate that DNN performs better results when compared to traditional approaches.
Journal ArticleDOI

Predicting electricity demand profiles of new supermarkets using machine learning

TL;DR: A data-driven method to predict the future ’electricity daily load profile’ (EDLP) of new supermarkets using historical EDLPs of existing supermarkets of the same type, which shows that supermarkets only with electricity are better predicted than supermarkets with electricity and gas.

Towards a Digital Twin Enabled Multifidelity Framework for Small Satellites

TL;DR: This work focuses on handling a constantstream of live data and uses a multi-fidelity approach to combine a low fidelity surrogate model with a high fidelity model to perform uncertainty quantification in order to perform fault detection.

Simulation of the Use of a Heat Accumulator in Combined Heat and Power Plants

TL;DR: In this paper, the authors presented the specific of the operation of a combined heat and power plant with a heat accumulator in the electricity market while taking the parameters affected by uncertainty into account.
Journal ArticleDOI

Meta-learning for few-shot time series forecasting

TL;DR: A meta-learning-based prediction mechanism for few-shot time series forecasting task, which mainly consists of meta-training and meta-testing, and has strong robustness for forecast horizons and data scales.
References
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Book

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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

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

F. Rosenblatt
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.
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