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Machine Learning for Energy Load Forecasting
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Machine Learning for Energy Load Forecasting

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: 28 February 2025 | Viewed by 4644

Special Issue Editor

Dr. Behnam Askarian

E-Mail Website
Guest Editor
College of Engineering, West Texas A&M University, Canyon, TX 79016, USA
Interests: renewable energy; energy storage systems; machine learning; image/signal processing; internet of things (IoT); biomedical engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue on “Machine Learning for Energy Load Forecasting” in Energies seeks to showcase the latest advancements in the application of machine learning (ML) techniques to predict energy demands, a critical component for enhancing the sustainability and efficiency of global energy systems. This edition calls for papers that break new ground in ML methodologies, tackle the intricacies of energy forecasting, and demonstrate the practical application of these technologies in real-world settings.

The scope of this Special Issue encompasses a wide array of topics, including the development of innovative ML algorithms for precise energy load forecasting, strategies for the integration of renewable energy sources using predictive models, and the exploration of deep learning techniques to decode complex energy consumption patterns. Contributions that provide a comparative analysis of ML approaches or offer case studies illustrating the implementation challenges and successes in smart grids, sustainable urban environments, and energy-efficient infrastructure are particularly encouraged.

Dr. Behnam Askarian
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

  • machine learning
  • energy load forecasting
  • renewable energy integration
  • deep learning applications
  • smart grids
  • energy management
  • real-time energy demand prediction
  • energy efficiency

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Published Papers (6 papers)

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Research

23 pages, 629 KiB  
Article
Few-Shot Load Forecasting Under Data Scarcity in Smart Grids: A Meta-Learning Approach
by Georgios Tsoumplekas, Christos Athanasiadis, Dimitrios I. Doukas, Antonios Chrysopoulos and Pericles Mitkas
Energies 2025, 18(3), 742; https://doi.org/10.3390/en18030742 - 6 Feb 2025
Viewed by 484
Abstract
Despite the rapid expansion of smart grids and large volumes of data at the individual consumer level, there are still various cases where adequate data collection to train accurate load forecasting models is challenging or even impossible. This paper proposes adapting an established [...] Read more.
Despite the rapid expansion of smart grids and large volumes of data at the individual consumer level, there are still various cases where adequate data collection to train accurate load forecasting models is challenging or even impossible. This paper proposes adapting an established Model-Agnostic Meta-Learning algorithm for short-term load forecasting in the context of few-shot learning. Specifically, the proposed method can rapidly adapt and generalize within any unknown load time series of arbitrary length using only minimal training samples. In this context, the meta-learning model learns an optimal set of initial parameters for a base-level learner recurrent neural network. The proposed model is evaluated using a dataset of historical load consumption data from real-world consumers. Despite the examined load series’ short length, it produces accurate forecasts outperforming transfer learning and task-specific machine learning methods by 12.5%. To enhance robustness and fairness during model evaluation, a novel metric, mean average log percentage error, is proposed that alleviates the bias introduced by the commonly used MAPE metric. Finally, a series of studies to evaluate the model’s robustness under different hyperparameters and time series lengths is also conducted, demonstrating that the proposed approach consistently outperforms all other models. Full article
(This article belongs to the Special Issue Machine Learning for Energy Load Forecasting)
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16 pages, 3952 KiB  
Article
Predictive Modeling of Energy Consumption for Cooling Ventilation in Livestock Buildings: A Machine Learning Approach
by Carlos Alejandro Perez Garcia, Patrizia Tassinari, Daniele Torreggiani and Marco Bovo
Energies 2025, 18(3), 633; https://doi.org/10.3390/en18030633 - 30 Jan 2025
Viewed by 482
Abstract
This research developed a predictive model using NeuralProphet to estimate energy consumption in the ventilation system of a dairy cattle farm. The necessity for energy management in livestock farming has increased due to the growing energy demands associated with climate control systems. Approximately [...] Read more.
This research developed a predictive model using NeuralProphet to estimate energy consumption in the ventilation system of a dairy cattle farm. The necessity for energy management in livestock farming has increased due to the growing energy demands associated with climate control systems. Approximately two years of historical energy consumption data, collected through a smart monitoring system deployed on the farm, were utilized as the primary input for the NeuralProphet model to predict long-term trends and seasonal variations. The computational results demonstrated satisfactory performance, achieving a coefficient of determination (R2) of 0.85 and a mean absolute error (MAE) of 27.47 kWh. The model effectively captured general trends and seasonal patterns, providing valuable insights into energy usage under existing operational conditions. However, short-term fluctuations were less accurately predicted due to the exclusion of exogenous climatic variables, such as temperature and humidity. The proposed model demonstrated superiority over traditional approaches in its capacity to forecast long-term energy demand, providing critical support for energy management and strategic decision-making in dairy farm operations. Full article
(This article belongs to the Special Issue Machine Learning for Energy Load Forecasting)
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11 pages, 248 KiB  
Article
Transfer Learning for the Prediction of Energy Performance of Water-Cooled Electric Chillers: Grey-Box Models Versus Deep Neural Network (DNN) Models
by Hongwen Dou and Radu Zmeureanu
Energies 2024, 17(23), 5981; https://doi.org/10.3390/en17235981 - 28 Nov 2024
Viewed by 462
Abstract
The development of data-driven prediction models of energy performance of HVAC equipment, such as chillers, depends on the quality and quantity of measurement data for the model training. The practical applications always struggle with the credibility of results when the training dataset of [...] Read more.
The development of data-driven prediction models of energy performance of HVAC equipment, such as chillers, depends on the quality and quantity of measurement data for the model training. The practical applications always struggle with the credibility of results when the training dataset of an existing chiller is relatively small. Moreover, when the energy analyst needs to develop a reliable predictive model of a new chiller, the manufacturer’s proprietary data are not always available. The transfer learning method can soften these constraints and can help in the development of a predictive model that captures the knowledge from the available chiller, called the source chiller, using a small dataset, and apply it to a new chiller, called the target chiller. The paper presents the successful application of transfer learning strategies by using grey-box models and DNN models for the prediction of chillers performance, when measurement data are recorded at 15 min time intervals by the building automation system (BAS) and used for training and testing. The paper confirms the initial hypothesis that both the grey-box models and DNN models of the source chiller from July 2013 predict well the energy performance of the target chiller with measurement datasets from 2016. The DNN models perform slightly better than the grey-box models. The pre-trained grey-box models and DNN models, respectively, are transferred to the target chiller using three strategies: SelfL, TLS0, and TLS1, and the results are compared. SelfL strategy trains and tests the models only with the target data. TLS0 strategy directly transfers the models from the source chiller to the target chiller. TLS1 strategy transfers the models, pre-trained with an extended dataset that is composed of training dataset of Ds and training dataset of Dt. Finally, the models are tested with another set of testing data. The difference in computation times of these two types of models is not significant for preventing the use of DNN models for the applications within the BAS, when compared with grey-box models. Full article
(This article belongs to the Special Issue Machine Learning for Energy Load Forecasting)
19 pages, 455 KiB  
Article
An Improved Decline Curve Analysis Method via Ensemble Learning for Shale Gas Reservoirs
by Yu Zhou, Zaixun Gu, Changyu He, Junwen Yang and Jian Xiong
Energies 2024, 17(23), 5910; https://doi.org/10.3390/en17235910 - 25 Nov 2024
Viewed by 668
Abstract
As a clean unconventional energy source, shale gas reservoirs are increasingly important globally. Accurate prediction methods for shale gas production capacity can bring significant economic benefits by reducing construction and operating costs. Decline curve analysis (DCA) is an efficient method that uses mathematical [...] Read more.
As a clean unconventional energy source, shale gas reservoirs are increasingly important globally. Accurate prediction methods for shale gas production capacity can bring significant economic benefits by reducing construction and operating costs. Decline curve analysis (DCA) is an efficient method that uses mathematical formulas to describe production trends with minimal reliance on geological or engineering parameters. However, traditional DCA models often fail to capture the complex production dynamics of shale gas wells, especially in complex environments. To overcome these limitations, this study proposes an Improved DCA method that integrates multiple base empirical DCA models through ensemble learning. By combining the strengths of individual models, it offers a more robust and accurate prediction framework. We evaluated this method using data from 22 shale gas wells in region L, China, comparing it to six traditional DCA models, including Arps and the Logistic Growth Model (LGM). The results show that the Improved DCA model achieved superior performance—with an mean absolute error (MAE) of 0.0660, an mean squared error (MSE) of 0.0272, and an R2 value of 0.9882—and exhibited greater stability across various samples and conditions. This method provides a reliable tool for long-term production forecasting and optimization without extensive geological or engineering information. Full article
(This article belongs to the Special Issue Machine Learning for Energy Load Forecasting)
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16 pages, 2417 KiB  
Article
Attention-Based Load Forecasting with Bidirectional Finetuning
by Firuz Kamalov, Inga Zicmane, Murodbek Safaraliev, Linda Smail, Mihail Senyuk and Pavel Matrenin
Energies 2024, 17(18), 4699; https://doi.org/10.3390/en17184699 - 21 Sep 2024
Viewed by 950
Abstract
Accurate load forecasting is essential for the efficient and reliable operation of power systems. Traditional models primarily utilize unidirectional data reading, capturing dependencies from past to future. This paper proposes a novel approach that enhances load forecasting accuracy by fine tuning an attention-based [...] Read more.
Accurate load forecasting is essential for the efficient and reliable operation of power systems. Traditional models primarily utilize unidirectional data reading, capturing dependencies from past to future. This paper proposes a novel approach that enhances load forecasting accuracy by fine tuning an attention-based model with a bidirectional reading of time-series data. By incorporating both forward and backward temporal dependencies, the model gains a more comprehensive understanding of consumption patterns, leading to improved performance. We present a mathematical framework supporting this approach, demonstrating its potential to reduce forecasting errors and improve robustness. Experimental results on real-world load datasets indicate that our bidirectional model outperforms state-of-the-art conventional unidirectional models, providing a more reliable tool for short and medium-term load forecasting. This research highlights the importance of bidirectional context in time-series forecasting and its practical implications for grid stability, economic efficiency, and resource planning. Full article
(This article belongs to the Special Issue Machine Learning for Energy Load Forecasting)
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28 pages, 11818 KiB  
Article
Enhancing Aggregate Load Forecasting Accuracy with Adversarial Graph Convolutional Imputation Network and Learnable Adjacency Matrix
by Junhao Zhao, Xiaodong Shen, Youbo Liu, Junyong Liu and Xisheng Tang
Energies 2024, 17(18), 4583; https://doi.org/10.3390/en17184583 - 12 Sep 2024
Viewed by 1023
Abstract
Accurate load forecasting, especially in the short term, is crucial for the safe and stable operation of power systems and their market participants. However, as modern power systems become increasingly complex, the challenges of short-term load forecasting are also intensifying. To address this [...] Read more.
Accurate load forecasting, especially in the short term, is crucial for the safe and stable operation of power systems and their market participants. However, as modern power systems become increasingly complex, the challenges of short-term load forecasting are also intensifying. To address this challenge, data-driven deep learning techniques and load aggregation technologies have gradually been introduced into the field of load forecasting. However, data quality issues persist due to various factors such as sensor failures, unstable communication, and susceptibility to network attacks, leading to data gaps. Furthermore, in the domain of aggregated load forecasting, considering the potential interactions among aggregated loads can help market participants engage in cross-market transactions. However, aggregated loads often lack clear geographical locations, making it difficult to predefine graph structures. To address the issue of data quality, this study proposes a model named adversarial graph convolutional imputation network (AGCIN), combined with local and global correlations for imputation. To tackle the problem of the difficulty in predefining graph structures for aggregated loads, this study proposes a learnable adjacency matrix, which generates an adaptive adjacency matrix based on the relationships between different sequences without the need for geographical information. The experimental results demonstrate that the proposed imputation method outperforms other imputation methods in scenarios with random and continuous missing data. Additionally, the prediction accuracy of the proposed method exceeds that of several baseline methods, affirming the effectiveness of our approach in imputation and prediction, ultimately enhancing the accuracy of aggregated load forecasting. Full article
(This article belongs to the Special Issue Machine Learning for Energy Load Forecasting)
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