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Time Series Forecasting for Energy Consumption
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Time Series Forecasting for Energy Consumption

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "I: Energy Fundamentals and Conversion".

Deadline for manuscript submissions: closed (2 April 2021) | Viewed by 21617

Special Issue Editor

Prof. Dr. María del Carmen Pegalajar Jiménez

E-Mail Website
Guest Editor
Department of Computer Science and Artificial Intelligence, ETS de Ingenierías Informática y de Telecomunicación (ETSIIT), Universidad de Granada, 18010 Granada, Andalusia, Spain
Interests: machine learning; pattern recognition; computational intelligence; neural networks; deep learning; evolutionary algorithms; artificial intelligence; applied artificial intelligence; fuzzy logic; energy consumption modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last few years, there has been considerable progress in time-series forecasting algorithms, which are becoming more and more accurate, and its applications are numerous and varied. Specifically, predicting accurately energy consumption in a particular building, country, etc. is an important task to properly manage energy efficiency. Moreover, it can be advantageous to carry this out in a short time taking into account the new consumption paradigm. On the other hand, the time horizon must be considered, which can be short, medium, or long-term. For this reason, it is important to develop and implement new intelligent models faster and more accurately. In this way, the application of Big Data and Machine Learning techniques have become essential to achieve this goal.

This Special Issue seeks to contribute to the advancement of energy consumption prediction using artificial intelligence models in an optimal and precise manner. We invite papers on innovative artificial intelligence applications to energy consumption forecasting, including reviews and case studies.

Prof. Dr. María del Carmen Pegalajar Jiménez
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • renewable energy, solar power, wind power
  • deep learning
  • artificial neural networks
  • data mining
  • netload forecasting
  • energy consumption forecasting
  • energy-related time series analysis
  • energy-related time series model
  • energy-related time series forecasting

Published Papers (7 papers)

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Editorial

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3 pages, 177 KiB  
Editorial
Time Series Forecasting for Energy Consumption
by M. C. Pegalajar and L. G. B. Ruiz
Energies 2022, 15(3), 773; https://doi.org/10.3390/en15030773 - 21 Jan 2022
Cited by 2 | Viewed by 1748
Abstract
Introduction In the last few years, there has been considerable progress in time series forecasting algorithms, which are becoming more and more accurate, and their applications are numerous and varied [...] Full article
(This article belongs to the Special Issue Time Series Forecasting for Energy Consumption)

Research

Jump to: Editorial

22 pages, 1074 KiB  
Article
A TensorFlow Approach to Data Analysis for Time Series Forecasting in the Energy-Efficiency Realm
by J. R. S. Iruela, L. G. B. Ruiz, M. I. Capel and M. C. Pegalajar
Energies 2021, 14(13), 4038; https://doi.org/10.3390/en14134038 - 04 Jul 2021
Cited by 12 | Viewed by 3157
Abstract
Thanks to advances in smart metering devices (SM), the electricity sector is undergoing a series of changes, among which it is worth highlighting the ability to control the response to all events that occur in the electricity grid with the intention of making [...] Read more.
Thanks to advances in smart metering devices (SM), the electricity sector is undergoing a series of changes, among which it is worth highlighting the ability to control the response to all events that occur in the electricity grid with the intention of making it more smart. Predicting electricity consumption data is a key factor for the energy sector in order to create a completely intelligent electricity grid that optimizes consumption and forecasts future energy needs. However, it is currently not enough to give a prediction of energy consumption (EC), but it is also necessary to give the prediction as fast as possible so that the grid can operate in the shortest possible time. An approach for developing EC prediction systems is introduced here by the use of artificial neural networks (ANN). Differently from other research studies on the subject, a divide-and-conquer strategy is used so that the target system’s execution switches from one to another specialized small models that forecast the EC of a building within the time range of one hour. By simultaneously processing a large amount of data and models, a consequence of implementing them in parallel with TensorFlow on GPUs, the training procedure proposed here increases the performance of the classic time series prediction methods, which are based on ANN. Leveraging the latest generation of ANN techniques and new GPU-based architectures, correct EC predictions can be obtained and, as the experimentation carried out in this work shows, such predictions can be obtained quickly. The obtained results in this study show a promising way for speeding up big data processing of building’s monitoring data to achieve energy efficiency. Full article
(This article belongs to the Special Issue Time Series Forecasting for Energy Consumption)
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16 pages, 3166 KiB  
Article
Short-Term Load Forecasting Using Encoder-Decoder WaveNet: Application to the French Grid
by Fernando Dorado Rueda, Jaime Durán Suárez and Alejandro del Real Torres
Energies 2021, 14(9), 2524; https://doi.org/10.3390/en14092524 - 28 Apr 2021
Cited by 28 | Viewed by 2898
Abstract
The prediction of time series data applied to the energy sector (prediction of renewable energy production, forecasting prosumers’ consumption/generation, forecast of country-level consumption, etc.) has numerous useful applications. Nevertheless, the complexity and non-linear behaviour associated with such kind of energy systems hinder the [...] Read more.
The prediction of time series data applied to the energy sector (prediction of renewable energy production, forecasting prosumers’ consumption/generation, forecast of country-level consumption, etc.) has numerous useful applications. Nevertheless, the complexity and non-linear behaviour associated with such kind of energy systems hinder the development of accurate algorithms. In such a context, this paper investigates the use of a state-of-art deep learning architecture in order to perform precise load demand forecasting 24-h-ahead in the whole country of France using RTE data. To this end, the authors propose an encoder-decoder architecture inspired by WaveNet, a deep generative model initially designed by Google DeepMind for raw audio waveforms. WaveNet uses dilated causal convolutions and skip-connection to utilise long-term information. This kind of novel ML architecture presents different advantages regarding other statistical algorithms. On the one hand, the proposed deep learning model’s training process can be parallelized in GPUs, which is an advantage in terms of training times compared to recurrent networks. On the other hand, the model prevents degradations problems (explosions and vanishing gradients) due to the residual connections. In addition, this model can learn from an input sequence to produce a forecast sequence in a one-shot manner. For comparison purposes, a comparative analysis between the most performing state-of-art deep learning models and traditional statistical approaches is presented: Autoregressive-Integrated Moving Average (ARIMA), Long-Short-Term-Memory, Gated-Recurrent-Unit (GRU), Multi-Layer Perceptron (MLP), causal 1D-Convolutional Neural Networks (1D-CNN) and ConvLSTM (Encoder-Decoder). The values of the evaluation indicators reveal that WaveNet exhibits superior performance in both forecasting accuracy and robustness. Full article
(This article belongs to the Special Issue Time Series Forecasting for Energy Consumption)
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23 pages, 531 KiB  
Article
A Higher Order Mining Approach for the Analysis of Real-World Datasets
by Shahrooz Abghari, Veselka Boeva, Jens Brage and Håkan Grahn
Energies 2020, 13(21), 5781; https://doi.org/10.3390/en13215781 - 04 Nov 2020
Cited by 2 | Viewed by 1741
Abstract
In this study, we propose a higher order mining approach that can be used for the analysis of real-world datasets. The approach can be used to monitor and identify the deviating operational behaviour of the studied phenomenon in the absence of prior knowledge [...] Read more.
In this study, we propose a higher order mining approach that can be used for the analysis of real-world datasets. The approach can be used to monitor and identify the deviating operational behaviour of the studied phenomenon in the absence of prior knowledge about the data. The proposed approach consists of several different data analysis techniques, such as sequential pattern mining, clustering analysis, consensus clustering and the minimum spanning tree (MST). Initially, a clustering analysis is performed on the extracted patterns to model the behavioural modes of the studied phenomenon for a given time interval. The generated clustering models, which correspond to every two consecutive time intervals, can further be assessed to determine changes in the monitored behaviour. In cases in which significant differences are observed, further analysis is performed by integrating the generated models into a consensus clustering and applying an MST to identify deviating behaviours. The validity and potential of the proposed approach is demonstrated on a real-world dataset originating from a network of district heating (DH) substations. The obtained results show that our approach is capable of detecting deviating and sub-optimal behaviours of DH substations. Full article
(This article belongs to the Special Issue Time Series Forecasting for Energy Consumption)
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19 pages, 3982 KiB  
Article
Very Short-Term Load Forecaster Based on a Neural Network Technique for Smart Grid Control
by Fermín Rodríguez, Fernando Martín, Luis Fontán and Ainhoa Galarza
Energies 2020, 13(19), 5210; https://doi.org/10.3390/en13195210 - 06 Oct 2020
Cited by 9 | Viewed by 2327
Abstract
Electrical load forecasting plays a crucial role in the proper scheduling and operation of power systems. To ensure the stability of the electrical network, it is necessary to balance energy generation and demand. Hence, different very short-term load forecast technologies are being designed [...] Read more.
Electrical load forecasting plays a crucial role in the proper scheduling and operation of power systems. To ensure the stability of the electrical network, it is necessary to balance energy generation and demand. Hence, different very short-term load forecast technologies are being designed to improve the efficiency of current control strategies. This paper proposes a new forecaster based on artificial intelligence, specifically on a recurrent neural network topology, trained with a Levenberg–Marquardt learning algorithm. Moreover, a sensitivity analysis was performed for determining the optimal input vector, structure and the optimal database length. In this case, the developed tool provides information about the energy demand for the next 15 min. The accuracy of the forecaster was validated by analysing the typical error metrics of sample days from the training and validation databases. The deviation between actual and predicted demand was lower than 0.5% in 97% of the days analysed during the validation phase. Moreover, while the root mean square error was 0.07 MW, the mean absolute error was 0.05 MW. The results suggest that the forecaster’s accuracy is considered sufficient for installation in smart grids or other power systems and for predicting future energy demand at the chosen sites. Full article
(This article belongs to the Special Issue Time Series Forecasting for Energy Consumption)
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18 pages, 4606 KiB  
Article
Industrial Facility Electricity Consumption Forecast Using Artificial Neural Networks and Incremental Learning
by Daniel Ramos, Pedro Faria, Zita Vale, João Mourinho and Regina Correia
Energies 2020, 13(18), 4774; https://doi.org/10.3390/en13184774 - 12 Sep 2020
Cited by 24 | Viewed by 2587
Abstract
Society’s concerns with electricity consumption have motivated researchers to improve on the way that energy consumption management is done. The reduction of energy consumption and the optimization of energy management are, therefore, two major aspects to be considered. Additionally, load forecast provides relevant [...] Read more.
Society’s concerns with electricity consumption have motivated researchers to improve on the way that energy consumption management is done. The reduction of energy consumption and the optimization of energy management are, therefore, two major aspects to be considered. Additionally, load forecast provides relevant information with the support of historical data allowing an enhanced energy management, allowing energy costs reduction. In this paper, the proposed consumption forecast methodology uses an Artificial Neural Network (ANN) and incremental learning to increase the forecast accuracy. The ANN is retrained daily, providing an updated forecasting model. The case study uses 16 months of data, split in 5-min periods, from a real industrial facility. The advantages of using the proposed method are illustrated with the numerical results. Full article
(This article belongs to the Special Issue Time Series Forecasting for Energy Consumption)
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16 pages, 4642 KiB  
Article
Time Series Forecasting with Multi-Headed Attention-Based Deep Learning for Residential Energy Consumption
by Seok-Jun Bu and Sung-Bae Cho
Energies 2020, 13(18), 4722; https://doi.org/10.3390/en13184722 - 10 Sep 2020
Cited by 36 | Viewed by 4545
Abstract
Predicting residential energy consumption is tantamount to forecasting a multivariate time series. A specific window for several sensor signals can induce various features extracted to forecast the energy consumption by using a prediction model. However, it is still a challenging task because of [...] Read more.
Predicting residential energy consumption is tantamount to forecasting a multivariate time series. A specific window for several sensor signals can induce various features extracted to forecast the energy consumption by using a prediction model. However, it is still a challenging task because of irregular patterns inside including hidden correlations between power attributes. In order to extract the complicated irregular energy patterns and selectively learn the spatiotemporal features to reduce the translational variance between energy attributes, we propose a deep learning model based on the multi-headed attention with the convolutional recurrent neural network. It exploits the attention scores calculated with softmax and dot product operation in the network to model the transient and impulsive nature of energy demand. Experiments with the dataset of University of California, Irvine (UCI) household electric power consumption consisting of a total 2,075,259 time-series show that the proposed model reduces the prediction error by 31.01% compared to the state-of-the-art deep learning model. Especially, the multi-headed attention improves the prediction performance even more by up to 27.91% than the single-attention. Full article
(This article belongs to the Special Issue Time Series Forecasting for Energy Consumption)
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