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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 13932

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Special Issue Editor

Dr. Piotr Kosowski

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Guest Editor

Department of Petroleum Engineering, AGH University of Science and Technology, 30-059 Krakow, Poland
Interests: machine learning; artificial intelligence; statistical modeling; energy; petroleum economics; capital budgeting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning and artificial intelligence are some of the most popular terms across all industries today. Our world is entering a new age driven by data and taking advantage of the opportunities offered by artificial intelligence (AI), while machine learning (ML) is becoming a business necessity. The energy industry is not an exception.

There is huge potential for ML and AI in energy matters. Their use may significantly accelerate the energy transition by creating an intelligent coordination layer across the generation, transmission, and use of energy. As a result, cost reduction, performance, and effectiveness increase, and better coordination and management may be achieved.

This Special Issue will deal with novel AI and ML applications in the energy sector. Topics of interest for publication include but are not limited to:

Data science in the energy industry;
Artificial Intelligence in the process of energy transformation and decarbonization;
Forecasting;
Smart grids;
Anomalies and failures in the energy industry;
Energy modeling;
AI and ML in energy geopolitics;
AI and ML in energy security;
Renewable energy and AI&ML
Maximization of energy efficiency with the use of AI and ML;
Intelligent prevention of energy theft;
Intelligent control of energy systems;
Intelligent energy generation;
Collection and use of data in the energy industry;
AI and ML, energy, and society;
AI and ML in energy-related research;
Big data in in the energy industry;
Design of materials, devices, and energy systems based on data;
The Internet of Things in the energy industry;
Virtual reality in energy;
AI and the human factor in the energy industry;
Energy robotics.

Dr. Piotr Kosowski
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
data science
energy security
renewables
big data
energy transformation
decarbonization
forecasting
smart grids
internet of things
virtual reality
modeling
energy efficiency

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Published Papers (6 papers)

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Research

27 pages, 8118 KiB

Open AccessArticle

Prediction of Electricity Generation Using Onshore Wind and Solar Energy in Germany

by Maciej Jakub Walczewski and Hendrik Wöhrle

Energies 2024, 17(4), 844; https://doi.org/10.3390/en17040844 - 10 Feb 2024

Cited by 5 | Viewed by 2885

Abstract

Renewable energy production is one of the most important strategies to reduce the emission of greenhouse gases. However, wind and solar energy especially depend on time-varying properties of the environment, such as weather. Hence, for the control and stabilization of electricity grids, the accurate forecasting of energy production from renewable energy sources is essential. This study provides an empirical comparison of the forecasting accuracy of electricity generation from renewable energy sources by different deep learning methods, including five different Transformer-based forecasting models based on weather data. The models are compared with the long short-term memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) models as a baseline. The accuracy of these models is evaluated across diverse forecast periods, and the impact of utilizing selected weather data versus all available data on predictive performance is investigated. Distinct performance patterns emerge among the Transformer-based models, with Autoformer and FEDformer exhibiting suboptimal results for this task, especially when utilizing a comprehensive set of weather parameters. In contrast, the Informer model demonstrates superior predictive capabilities for onshore wind power and photovoltaic (PV) power production. The Informer model consistently performs well in predicting both onshore wind and PV energy. Notably, the LSTM model outperforms all other models across various categories. This research emphasizes the significance of selectively using weather parameters for improved performance compared to employing all parameters and a time reference. We show that the suitability and performance of a prediction model can vary significantly, depending on the specific forecasting task and the data that are provided to the model. Full article

(This article belongs to the Special Issue Energy – Machine Learning and Artificial Intelligence)

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24 pages, 8231 KiB

Open AccessArticle

A Regression Framework for Energy Consumption in Smart Cities with Encoder-Decoder Recurrent Neural Networks

by Berny Carrera and Kwanho Kim

Energies 2023, 16(22), 7508; https://doi.org/10.3390/en16227508 - 9 Nov 2023

Viewed by 1460

Abstract

Currently, a smart city should ideally be environmentally friendly and sustainable, and energy management is one method to monitor sustainable use. This research project investigates the potential for a “smart city” to improve energy management by enabling the adoption of various types of intelligent technology to improve the energy sustainability of a city’s infrastructure and operational efficiency. In addition, the South Korean smart city region of Songdo serves as the inspiration for this case study. In the first module of the proposed framework, we place a strong emphasis on the data capabilities necessary to generate energy statistics for each of the numerous structures. In the second phase of the procedure, we employ the collected data to conduct a data analysis of the energy behavior within the microcities, from which we derive characteristics. In the third module, we construct baseline regressors to assess the proposed model’s varying degrees of efficacy. Finally, we present a method for building an energy prediction model using a deep learning regression model to solve the problem of 48-hour-ahead energy consumption forecasting. The recommended model is preferable to other models in terms of R², MAE, and RMSE, according to the study’s findings. Full article

(This article belongs to the Special Issue Energy – Machine Learning and Artificial Intelligence)

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28 pages, 5027 KiB

Open AccessArticle

Primary Energy Consumption Patterns in Selected European Countries from 1990 to 2021: A Cluster Analysis Approach

by Piotr Kosowski, Katarzyna Kosowska and Damian Janiga

Energies 2023, 16(19), 6941; https://doi.org/10.3390/en16196941 - 4 Oct 2023

Cited by 6 | Viewed by 1999

Abstract

This study delves into the structure of primary energy consumption in European countries, utilizing data from the Eurostat database, and focuses on the years 1990 and 2021. Through cluster analysis, countries were categorized based on their consumption patterns, revealing significant insights into energy security. The findings indicate a discernible shift away from solid fossil fuels, with renewable energy sources witnessing the most substantial growth. Natural gas, serving as a transitional fuel, has seen a rise in consumption, while nuclear energy’s development remained relatively stagnant. Oil, despite its declining share, remains a crucial component in the European energy mix. The study also highlights the challenges and implications of over-reliance on a single energy source, emphasizing the need for a diversified energy strategy. The analysis underscores the importance of diversifying primary energy sources to ensure energy security. While renewable sources are environmentally favorable, their inherent instability necessitates backup from other energy sources. Solid fossil fuels, despite their availability, face challenges due to environmental concerns. Natural gas, while flexible, requires extensive infrastructure and is highly politicized. Nuclear energy, despite its potential as an ideal complement to renewables, faces barriers in terms of investment and public perception. Oil, though convenient, is a fossil source with associated CO₂ emissions and largely needs to be imported. In conclusion, the study advocates for a well-diversified set of energy sources tailored to individual country-specific situations, emphasizing the importance of strategic planning in energy consumption to ensure long-term energy security. Full article

(This article belongs to the Special Issue Energy – Machine Learning and Artificial Intelligence)

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16 pages, 2030 KiB

Open AccessArticle

Predicting Biomass Yields of Advanced Switchgrass Cultivars for Bioenergy and Ecosystem Services Using Machine Learning

by Jules F. Cacho, Jeremy Feinstein, Colleen R. Zumpf, Yuki Hamada, Daniel J. Lee, Nictor L. Namoi, DoKyoung Lee, Nicholas N. Boersma, Emily A. Heaton, John J. Quinn and Cristina Negri

Energies 2023, 16(10), 4168; https://doi.org/10.3390/en16104168 - 18 May 2023

Cited by 6 | Viewed by 1773

Abstract

The production of advanced perennial bioenergy crops within marginal areas of the agricultural landscape is gaining interest due to its potential to sustainably produce feedstocks for biofuels and bioproducts while also improving the sustainability and resilience of commodity crop production. However, predicting the biomass yields of this production system is challenging because marginal areas are often relatively small and spread around agricultural fields and are typically associated with various abiotic conditions that limit crop production. Machine learning (ML) offers a viable solution as a biomass yield prediction tool because it is suited to predicting relationships with complex functional associations. The objectives of this study were to (1) evaluate the accuracy of commonly applied ML algorithms in agricultural applications for predicting the biomass yields of advanced switchgrass cultivars for bioenergy and ecosystem services and (2) determine the most important biomass yield predictors. Datasets on biomass yield, weather, land marginality, soil properties, and agronomic management were generated from three field study sites in two U.S. Midwest states (Illinois and Iowa) over three growing seasons. The ML algorithms evaluated in the study included random forests (RFs), gradient boosting machines (GBMs), artificial neural networks (ANNs), K-neighbors regressor (KNR), AdaBoost regressor (ABR), and partial least squares regression (PLSR). Coefficient of determination (R²) and mean absolute error (MAE) were used to evaluate the predictive accuracy of the tested algorithms. Results showed that the ensemble methods, RF (R² = 0.86, MAE = 0.62 Mg/ha), GBM (R² = 0.88, MAE = 0.57 Mg/ha), and GBM (R² = 0.78, MAE = 0.66 Mg/ha), were the most accurate in predicting biomass yields of the Independence, Liberty, and Shawnee switchgrass cultivars, respectively. This is in agreement with similar studies that apply ML to multi-feature problems where traditional statistical methods are less applicable and datasets used were considered to be relatively small for ANNs. Consistent with previous studies on switchgrass, the most important predictors of biomass yield included average annual temperature, average growing season temperature, sum of the growing season precipitation, field slope, and elevation. This study helps pave the way for applying ML as a management tool for alternative bioenergy landscapes where understanding agronomic and environmental performance of a multifunctional cropping system seasonally and interannually at the sub-field scale is critical. Full article

(This article belongs to the Special Issue Energy – Machine Learning and Artificial Intelligence)

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24 pages, 11244 KiB

Open AccessArticle

Machine Learning Algorithms for Identifying Dependencies in OT Protocols

by Milosz Smolarczyk, Jakub Pawluk, Alicja Kotyla, Sebastian Plamowski, Katarzyna Kaminska and Krzysztof Szczypiorski

Energies 2023, 16(10), 4056; https://doi.org/10.3390/en16104056 - 12 May 2023

Viewed by 1838

Abstract

This study illustrates the utility and effectiveness of machine learning algorithms in identifying dependencies in data transmitted in industrial networks. The analysis was performed for two different algorithms. The study was carried out for the XGBoost (Extreme Gradient Boosting) algorithm based on a set of decision tree model classifiers, and the second algorithm tested was the EBM (Explainable Boosting Machines), which belongs to the class of Generalized Additive Models (GAM). Tests were conducted for several test scenarios. Simulated data from static equations were used, as were data from a simulator described by dynamic differential equations, and the final one used data from an actual physical laboratory bench connected via Modbus TCP/IP. Experimental results of both techniques are presented, thus demonstrating the effectiveness of the algorithms. The results show the strength of the algorithms studied, especially against static data. For dynamic data, the results are worse, but still at a level that allows using the researched methods to identify dependencies. The algorithms presented in this paper were used as a passive protection layer of a commercial IDS (Intrusion Detection System). Full article

(This article belongs to the Special Issue Energy – Machine Learning and Artificial Intelligence)

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15 pages, 3430 KiB

Open AccessArticle

A Novel Model for Spot Price Forecast of Natural Gas Based on Temporal Convolutional Network

by Yadong Pei, Chiou-Jye Huang, Yamin Shen and Mingyue Wang

Energies 2023, 16(5), 2321; https://doi.org/10.3390/en16052321 - 28 Feb 2023

Cited by 10 | Viewed by 2691

Abstract

Natural gas is often said to be the most environmentally friendly fossil fuel. Its usage has increased significantly in recent years. Meanwhile, accurate forecasting of natural gas spot prices has become critical to energy management, economic growth, and environmental protection. This work offers a novel model based on the temporal convolutional network (TCN) and dynamic learning rate for predicting natural gas spot prices over the following two weekdays. The residual block structure of TCN provides good prediction accuracy, and the dilated causal convolutions minimize the amount of computation. The dynamic learning rate setting was adopted to enhance the model’s prediction accuracy and robustness. Compared with three existing models, i.e., the one-dimensional convolutional neural network (1D-CNN), gate recurrent unit (GRU), and long short-term memory (LSTM), the proposed model can achieve better performance over other models with mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean squared error (RMSE) scores of 4.965%, 0.216, and 0.687, respectively. These attractive advantages make the proposed model a promising candidate for long-term stability in natural gas spot price forecasting. Full article

(This article belongs to the Special Issue Energy – Machine Learning and Artificial Intelligence)

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