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Applied Sciences
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Artificial Intelligence and Digital Technology in Smart Energy Systems
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Artificial Intelligence and Digital Technology in Smart Energy Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: 20 July 2025 | Viewed by 1463

Special Issue Editor

Dr. Hao Yu

E-Mail Website
Guest Editor
Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
Interests: distributed power generation and microgrid; intelligent distribution network operation analysis and optimal control

Special Issue Information

Dear Colleagues,

This Special Issue on “Artificial Intelligence and Digital Technology in Smart Energy Systems” aims to explore the latest advancements and applications of AI and digital technology in the field of smart energy systems. We welcome research articles, reviews, or other studies that address the use of AI and digital technology to improve the efficiency, reliability, and sustainability of energy systems. Topics of interest include but are not limited to smart grid optimization, energy forecasting, demand response, energy management systems, and grid integration of renewable energy sources. This Special Issue will serve as a platform for exchanging ideas and knowledge, ultimately contributing to the development of more intelligent and sustainable energy solutions.

Dr. Hao Yu
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. Applied Sciences 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 2400 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

  • smart energy system
  • energy forecasting
  • renewable energy

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Published Papers (2 papers)

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Research

13 pages, 1780 KiB  
Article
Forecasting Day-Ahead Electricity Demand in Australia Using a CNN-LSTM Model with an Attention Mechanism
by Laial Alsmadi, Gang Lei and Li Li
Appl. Sci. 2025, 15(7), 3829; https://doi.org/10.3390/app15073829 - 31 Mar 2025
Viewed by 29
Abstract
Accurate energy demand forecasting is vital for optimizing resource allocation and energy efficiency. Despite advancements in various prediction models, existing approaches often struggle to capture the complex, nonlinear relationships between temperature variations and electricity consumption. To address this issue, this paper introduces a [...] Read more.
Accurate energy demand forecasting is vital for optimizing resource allocation and energy efficiency. Despite advancements in various prediction models, existing approaches often struggle to capture the complex, nonlinear relationships between temperature variations and electricity consumption. To address this issue, this paper introduces a novel hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks with an attention mechanism designed to forecast day-ahead electricity demand in Australia. This research aims to enhance the accuracy of electricity demand predictions by effectively modeling the impact of heating degree days (HDDs) and cooling degree days (CDDs) on energy usage. The HDDs and CDDs capture extreme weather conditions. They are critical for understanding spikes in energy consumption for heating and cooling needs. The proposed model leverages the strengths of CNNs in extracting spatial features in HDDs and CDDs, LSTMs in capturing temporal dependencies, and the attention mechanism in focusing on the most relevant aspects of the data. This study compares the CNN-LSTM-Attention model with traditional methods, including Deep Neural Networks, and demonstrates superior performance. The results show a significant reduction in both Mean Absolute Error and Mean Absolute Percentage Error, confirming the model’s effectiveness. The primary contribution of this paper lies in the novel integration of CDD and HDD data within the CNN-LSTM framework, which has not been extensively explored in prior studies. This approach offers a robust solution for energy management, particularly in climates with significant temperature fluctuations. Full article
(This article belongs to the Special Issue Artificial Intelligence and Digital Technology in Smart Energy Systems)
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28 pages, 7586 KiB  
Article
A Comprehensive Hybrid Deep Learning Approach for Accurate Status Predicting of Hydropower Units
by Liyong Ma, Siqi Chen, Dali Wei, Yanshuo Zhang and Yinuo Guo
Appl. Sci. 2024, 14(20), 9323; https://doi.org/10.3390/app14209323 - 13 Oct 2024
Viewed by 1023
Abstract
Hydropower units are integral to sustainable energy production, and their operational reliability hinges on accurate status prediction. This paper introduces an innovative hybrid deep learning model that synergistically integrates a Temporal Convolutional Network (TCN), a Residual Short-Term LSTM (REST-LSTM) network, a Gated Recurrent [...] Read more.
Hydropower units are integral to sustainable energy production, and their operational reliability hinges on accurate status prediction. This paper introduces an innovative hybrid deep learning model that synergistically integrates a Temporal Convolutional Network (TCN), a Residual Short-Term LSTM (REST-LSTM) network, a Gated Recurrent Unit (GRU) network, and the tuna swarm optimization (TSO) algorithm. The model was meticulously designed to capture and utilize temporal features inherent in time series data, thereby enhancing predictive performance. Specifically, the TCN effectively extracts critical temporal features, while the REST-LSTM, with its residual connections, improves the retention of short-term memory in sequence data. The parallel incorporation of GRU further refines temporal dynamics, ensuring comprehensive feature capture. The TSO algorithm was employed to optimize the model’s parameters, leading to superior performance. The model’s efficacy was empirically validated using three datasets—unit flow rate, guide vane opening, and maximum guide vane water temperature—sourced from the Huadian Electric Power Research Institute. The experimental results demonstrate that the proposed model significantly reduces both the maximum and average prediction errors, while also offering substantial improvements in forecasting accuracy compared with the existing methodologies. This research presents a robust framework for hydropower unit operation prediction, advancing the application of deep learning in the hydropower sector. Full article
(This article belongs to the Special Issue Artificial Intelligence and Digital Technology in Smart Energy Systems)
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