Toward efficient energy systems based on natural gas consumption prediction with LSTM Recurrent Neural Networks

Laib, Oussama; Khadir, Mohamed Tarek; Mihaylova, Lyudmila

doi:10.1016/j.energy.2019.04.075

Cited by 105 publications

(30 citation statements)

References 49 publications

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“…Since then, published studies have been applied to natural gas-based target variables. In 2019, Laib et al explored the application of DFFNN and a LSTM models in order to forecast the next day gas consumption for the residential and industrial sectors [47]. Clustering was used to partition the data, after which and MLP would decide on which LSTM model to best handle the forecast.…”

Section: Sector Levelmentioning

confidence: 99%

A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings

Runge

Zmeureanu

2021

Energies

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Buildings account for a significant portion of our overall energy usage and associated greenhouse gas emissions. With the increasing concerns regarding climate change, there are growing needs for energy reduction and increasing our energy efficiency. Forecasting energy use plays a fundamental role in building energy planning, management and optimization. The most common approaches for building energy forecasting include physics and data-driven models. Among the data-driven models, deep learning techniques have begun to emerge in recent years due to their: improved abilities in handling large amounts of data, feature extraction characteristics, and improved abilities in modelling nonlinear phenomena. This paper provides an extensive review of deep learning-based techniques applied to forecasting the energy use in buildings to explore its effectiveness and application potential. First, we present a summary of published literature reviews followed by an overview of deep learning-based definitions and techniques. Next, we present a breakdown of current trends identified in published research along with a discussion of how deep learning-based models have been applied for feature extraction and forecasting. Finally, the review concludes with current challenges faced and some potential future research directions.

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Section: Sector Levelmentioning

confidence: 99%

A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings

Runge

Zmeureanu

2021

Energies

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“…As a result of the review of the scientific literature, regarding the implementation of techniques based on Machine Learning, for the approach of predictive solutions supported in time series, it was identified that the “Multi-Layered Perceptron” technique of the category Artificial Neural Networks has produced very good results in different fields of action, such as: investment models based on mutual funds [ 34 , 35 ], epidemiological models [ 35 , 36 ], estimation of the water recharge rate underground [ 37 , 38 ], analysis of the pedals interactions of race car drivers [ 39 ], efficient energy systems based on the prediction of natural gas consumption [ 40 , 41 ], and money flow prediction [ 42 ], among other studies.…”

Section: Contributionsmentioning

confidence: 99%

SISME, Estuarine Monitoring System Based on IOT and Machine Learning for the Detection of Salt Wedge in Aquifers: Case Study of the Magdalena River Estuary

Ariza-Colpas

Ayala-Mantilla²,

Shaheen

et al. 2021

Sensors

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This article contains methods, results, and analysis agreed for the development of an application based on the internet of things and making use of machine learning techniques that serves as a support for the identification of the saline wedge in the Magdalena River estuary, Colombia. As a result of this investigation, the process of identifying the most suitable telecommunications architecture to be installed in the estuary is shown, as well as the characteristics of the software developed called SISME (Estuary Monitoring System), and the results obtained after the implementation of prediction techniques based on time series. This implementation supports the maritime security of the port of Barranquilla since it can support decision-making related to the estuary. This research is the result of the project “Implementation of a Wireless System of Temperature, Conductivity and Pressure Sensors to support the identification of the saline wedge and its impact on the maritime safety of the Magdalena River estuary”.

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“…More recent studies presented numerous techniques for NG demand forecasting, including computational intelligence-based models (ANNs), fuzzy logic and support vector machines [12,[73][74][75][76]. In this sense, a combination of recurrent neural network and linear regression model was used in [77] to generate forecasts for future gas demand, whereas a multilayered perceptron (MLP) neural network was deployed in [78] to estimate the next day gas consumption. A day-ahead forecast was also examined in [79] by developing a functional autoregressive model with exogenous variables (FARX).…”

Section: Indicative Related Work On Ai Applied In Natural Gas Consumpmentioning

confidence: 99%

Forecasting of Day-Ahead Natural Gas Consumption Demand in Greece Using Adaptive Neuro-Fuzzy Inference System

et al. 2020

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(1) Background: Forecasting of energy consumption demand is a crucial task linked directly with the economy of every country all over the world. Accurate natural gas consumption forecasting allows policy makers to formulate natural gas supply planning and apply the right strategic policies in this direction. In order to develop a real accurate natural gas (NG) prediction model for Greece, we examine the application of neuro-fuzzy models, which have recently shown significant contribution in the energy domain. (2) Methods: The adaptive neuro-fuzzy inference system (ANFIS) is a flexible and easy to use modeling method in the area of soft computing, integrating both neural networks and fuzzy logic principles. The present study aims to develop a proper ANFIS architecture for time series modeling and prediction of day-ahead natural gas demand. (3) Results: An efficient and fast ANFIS architecture is built based on neuro-fuzzy exploration performance for energy demand prediction using historical data of natural gas consumption, achieving a high prediction accuracy. The best performing ANFIS method is also compared with other well-known artificial neural networks (ANNs), soft computing methods such as fuzzy cognitive map (FCM) and their hybrid combination architectures for natural gas prediction, reported in the literature, to further assess its prediction performance. The conducted analysis reveals that the mean absolute percentage error (MAPE) of the proposed ANFIS architecture results is less than 20% in almost all the examined Greek cities, outperforming ANNs, FCMs and their hybrid combination; and (4) Conclusions: The produced results reveal an improved prediction efficacy of the proposed ANFIS-based approach for the examined natural gas case study in Greece, thus providing a fast and efficient tool for utterly accurate predictions of future short-term natural gas demand.

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Toward efficient energy systems based on natural gas consumption prediction with LSTM Recurrent Neural Networks

Cited by 105 publications

References 49 publications

A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings

A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings

SISME, Estuarine Monitoring System Based on IOT and Machine Learning for the Detection of Salt Wedge in Aquifers: Case Study of the Magdalena River Estuary

Forecasting of Day-Ahead Natural Gas Consumption Demand in Greece Using Adaptive Neuro-Fuzzy Inference System

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