Machine Learning, Deep Learning and Statistical Analysis for forecasting building energy consumption — A systematic review

Khalil, Mohamad; McGough, Andrew Stephen; Pourmirza, Zoya; Pazhoohesh, Mehdi; Walker, Sara

doi:10.1016/j.engappai.2022.105287

Cited by 109 publications

(29 citation statements)

References 105 publications

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“…In this work, was set to 168 and n was set from 0 to 23 to conduct the experiments. Based on time-series prediction, the problem can be expressed as Equation (8). The prediction problem was a problem of continuously predicting data from 1 h to 24 h. For example, in the problem of predicting 10 h, 168 time data were input, and the predicted value from 1 h to 10 h was later compared with the correct value.…”

Section: Training Methodsmentioning

confidence: 99%

“…Recently, artificial intelligence technologies have been used to predict irregular patterns in various areas, such as power consumption and power generation. In particular, deep learning models based on neural networks play an important role because they make it easy to understand trends or characteristics of data, so research on applying deep learning technologies to the electric power field has been progressing steadily [5][6][7][8][9]. Accurately predicting the demand and supply of electrical energy or reducing its usage can reduce energy wastage.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Model Performance and Optimal Model Study for Hourly Fine Power Consumption Prediction

Oh,

Shin

et al. 2023

Electronics

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Electricity consumption has been increasing steadily owing to technological developments since the Industrial Revolution. Technologies that can predict power usage and management for improved efficiency are thus emerging. Detailed energy management requires precise power consumption forecasting. Deep learning technologies have been widely used recently to achieve high performance. Many deep learning technologies are focused on accuracy, but they do not involve detailed time-based usage prediction research. In addition, detailed power prediction models should consider computing power, such as that of end Internet of Things devices and end home AMIs. In this work, we conducted experiments to predict hourly demands for the temporal neural network (TCN) and transformer models, as well as artificial neural network, long short-term memory (LSTM), and gated recurrent unit models. The study covered detailed time intervals from 1 to 24 h with 1 h increments. The experimental results were analyzed, and the optimal models for different time intervals and datasets were derived. The LSTM model showed superior performance for datasets with characteristics similar to those of schools, while the TCN model performed better for average or industrial power consumption datasets.

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Section: Training Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning Model Performance and Optimal Model Study for Hourly Fine Power Consumption Prediction

Oh,

Shin

et al. 2023

Electronics

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“…The establishment of this hyperparameter is necessary since the central point between two synthesis embeddings presents overlap in the receptive field of both points. Therefore, the value of 1 2 makes each synthesis embedding acquire a maximum of similarity with that central point that allows the final sum of the projections of the receptive fields to produce values approximately bounded between [0, 1] for the entire S series.…”

Section: ) Synthesis Exponentmentioning

confidence: 99%

“…Among the different possible granularities with which the power production time series of photovoltaic plants can be expressed, the hourly, quarter-hourly, 5-minute, and minute granularities stand out; although others can be found without being limited to a fixed granularity. As stated in [1], it is common to find studies on energy forecasting in various granularities, with the most commonly used granularity being hourly. In addition, it is also possible to find power production time series whose granularity varies over time, for example due to an improvement in the equipment that monitors energy production.…”

Section: Introductionmentioning

confidence: 99%

A Granularity Invariant Method for the Classification of Energy Production Time Series in Photovoltaic Plants

Centeno¹,

García-Ordás²,

García-Olalla³

et al. 2023

IEEE Access

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The treatment of photovoltaic power production time series often faces the challenge of unifying the granularity of the series when generating a predictive model. This can limit the generation of a dataset in terms of the time covered and the number of examples. In addition, models built with data of static granularities tend to show rigidity when facing granularity variations, invalidating them for scenarios different from those of the data they were trained on. To address this issue, this paper presents a novel method specifically indicated for Deep-Learning models that shows invariance to granularity called Synthesis. This operation can be added as a layer to an artificial neural network, allowing it to be applied to any power production time series and synthesizing the content of an arbitrarily long time series into a fixed-size vector which can be used for classification or regression regardless of the initial time series length. The experiments with the NIST Campus Photovoltaic dataset demonstrate the effectiveness of the method, showing an F1-Score of 1.0 for the classification of series with granularities between 2 minutes and 2 hours, and an F-Score of 1.0 for the classification of time series with variations of granularity throughout time when training with 5-minute granularity samples.

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“…In the past few years, cyber-physical systems (CPS) protection has become increasingly important as new technologies in Industry 4.0 have emerged. Interaction between the “cyber” and “physical” worlds is achieved through the development of industrial systems for many different purposes: food industry ( Geng et al, 2022 ; Avila et al, 2019 ); autonomous vehicles ( Sharma, Sahoo & Puhan, 2021 ; Hou et al, 2022 ) agriculture ( Drury et al, 2017 ) or energy consumption forecasting ( Khalil et al, 2022 ; Feng et al, 2023 ). Artificial intelligence (AI) has recently enabled the development of more complex engineering applications capable of extracting knowledge from the large volumes of data generated by CPS ( Radanliev et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

A detailed study of resampling algorithms for cyberattack classification in engineering applications

Mogollón Gutiérrez,

Sancho Núñez,

Ávila

et al. 2024

PeerJ Computer Science

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The evolution of engineering applications is highly relevant in the context of protecting industrial systems. As industries are increasingly interconnected, the need for robust cybersecurity measures becomes paramount. Engineering informatics not only provides tools for knowledge representation and extraction but also affords a comprehensive spectrum of developing sophisticated cybersecurity solutions. However, safeguarding industrial systems poses a unique challenge due to the inherent heterogeneity of data within these environments. Together with this problem, it’s crucial to acknowledge that datasets that simulate real cyberattacks within these diverse environments exhibit a high imbalance, often skewed towards certain types of traffics. This study proposes a system for addressing class imbalance in cybersecurity. To do this, three oversampling (SMOTE, Borderline1-SMOTE, and ADASYN) and five undersampling (random undersampling, cluster centroids, NearMiss, repeated edited nearest neighbor, and Tomek Links) methods are tested. Particularly, these balancing algorithms are used to generate one-vs-rest binary models and to develop a two-stage classification system. By doing so, this study aims to enhance the efficacy of cybersecurity measures ensuring a more comprehensive understanding and defense against the diverse range of threats encountered in industrial environments. Experimental results demonstrates the effectiveness of proposed system for cyberattack detection and classification among nine widely known cyberattacks.

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Machine Learning, Deep Learning and Statistical Analysis for forecasting building energy consumption — A systematic review

Cited by 109 publications

References 105 publications

Deep Learning Model Performance and Optimal Model Study for Hourly Fine Power Consumption Prediction

Deep Learning Model Performance and Optimal Model Study for Hourly Fine Power Consumption Prediction

A Granularity Invariant Method for the Classification of Energy Production Time Series in Photovoltaic Plants

A detailed study of resampling algorithms for cyberattack classification in engineering applications

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