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Application of AI in Energy Savings and CO2 Reduction
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Application of AI in Energy Savings and CO2 Reduction

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "B3: Carbon Emission and Utilization".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 4992

Special Issue Editor

Prof. Dr. Francis F. Assadian

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Guest Editor
Department of Mechanical and Aerospace Engineering, University of California Davis, Davis, CA 95616, USA
Interests: autonomous vehicles; alternative powertrain; robust control; energetic modelling; application of artificial intelligence and machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

I am pleased to announce a new Special Issue, titled "Application of AI in Energy Savings and CO2 Reduction".

Reductions in energy usage and CO2 mitigation solutions present ongoing challenges in which new technologies can facilitate and improve these efforts. Consequently, the intent of this Special Issue is to present the current research with respect to how new technologies, namely, artificial intelligence and machine learning, have contributed to energy savings and CO2 emission reductions in engineering fields. Additionally, the application of current techniques and their significance will be addressed. This Special Issue will highlight the results of current research and development activities of the use of AI and machine learning in energy savings and CO2 reduction in the following areas: (1) transportation including ground vehicles and aerospace, (2) robotics and manufacturing, (3) industrial processes, (4) residential and institutional facilities, (5) power generation, and (6) agriculture. It is our objective to provide directional guidance within these research investigations to later serve as a valuable source for researchers’ future exploration. This Special Issue will contribute to the current knowledge base by presenting insights into intelligent techniques in the aforementioned areas to reduce energy usage and to mitigate CO2 emissions.

Prof. Dr. Francis F. Assadian
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.

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Published Papers (4 papers)

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Research

17 pages, 3159 KiB  
Article
A Novel Approach for Predicting CO2 Emissions in the Building Industry Using a Hybrid Multi-Strategy Improved Particle Swarm Optimization–Long Short-Term Memory Model
by Yuyi Hu, Bojun Wang, Yanping Yang and Liwei Yang
Energies 2024, 17(17), 4379; https://doi.org/10.3390/en17174379 - 1 Sep 2024
Cited by 1 | Viewed by 492
Abstract
The accurate prediction of carbon dioxide (CO2) emissions in the building industry can provide data support and theoretical insights for sustainable development. This study proposes a hybrid model for predicting CO2 emissions that combines a multi-strategy improved particle swarm optimization [...] Read more.
The accurate prediction of carbon dioxide (CO2) emissions in the building industry can provide data support and theoretical insights for sustainable development. This study proposes a hybrid model for predicting CO2 emissions that combines a multi-strategy improved particle swarm optimization (MSPSO) algorithm with a long short-term memory (LSTM) model. Firstly, the particle swarm optimization (PSO) algorithm is enhanced by combining tent chaotic mapping, mutation for the least-fit particles, and a random perturbation strategy. Subsequently, the performance of the MSPSO algorithm is evaluated using a set of 23 internationally recognized test functions. Finally, the predictive performance of the MSPSO-LSTM hybrid model is assessed using data from the building industry in the Yangtze River Delta region as a case study. The results indicate that the coefficient of determination (R2) of the model reaches 0.9677, which is more than 10% higher than that of BP, LSTM, and CNN non-hybrid models and demonstrates significant advantages over PSO-LSTM, GWO-LSTM, and WOA-LSTM hybrid models. Additionally, the mean square error (MSE) of the model is 2445.6866 Mt, and the mean absolute error (MAE) is 4.1010 Mt, both significantly lower than those of the BP, LSTM, and CNN non-hybrid models. Overall, the MSPSO-LSTM hybrid model demonstrates high predictive accuracy for CO2 emissions in the building industry, offering robust support for the sustainable development of the industry. Full article
(This article belongs to the Special Issue Application of AI in Energy Savings and CO2 Reduction)
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16 pages, 4637 KiB  
Article
PV-Optimized Heat Pump Control in Multi-Family Buildings Using a Reinforcement Learning Approach
by Michael Bachseitz, Muhammad Sheryar, David Schmitt, Thorsten Summ, Christoph Trinkl and Wilfried Zörner
Energies 2024, 17(8), 1908; https://doi.org/10.3390/en17081908 - 17 Apr 2024
Viewed by 1021
Abstract
For the energy transition in the residential sector, heat pumps are a core technology for decarbonizing thermal energy production for space heating and domestic hot water. Electricity generation from on-site photovoltaic (PV) systems can also contribute to a carbon-neutral building stock. However, both [...] Read more.
For the energy transition in the residential sector, heat pumps are a core technology for decarbonizing thermal energy production for space heating and domestic hot water. Electricity generation from on-site photovoltaic (PV) systems can also contribute to a carbon-neutral building stock. However, both will increase the stress on the electricity grid. This can be reduced by using appropriate control strategies to match electricity consumption and production. In recent years, artificial intelligence-based approaches such as reinforcement learning (RL) have become increasingly popular for energy-system management. However, the literature shows a lack of investigation of RL-based controllers for multi-family building energy systems, including an air source heat pump, thermal storage, and a PV system, although this is a common system configuration. Therefore, in this study, a model of such an energy system and RL-based controllers were developed and simulated with physical models and compared with conventional rule-based approaches. Four RL algorithms were investigated for two objectives, and finally, the soft actor–critic algorithm was selected for the annual simulations. The first objective, to maintain only the required temperatures in the thermal storage, could be achieved by the developed RL agent. However, the second objective, to additionally improve the PV self-consumption, was better achieved by the rule-based controller. Therefore, further research on the reward function, hyperparameters, and advanced methods, including long short-term memory layers, as well as a training for longer time periods than six days are suggested. Full article
(This article belongs to the Special Issue Application of AI in Energy Savings and CO2 Reduction)
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27 pages, 10993 KiB  
Article
A Trip-Based Data-Driven Model for Predicting Battery Energy Consumption of Electric City Buses
by Zvonimir Dabčević, Branimir Škugor, Ivan Cvok and Joško Deur
Energies 2024, 17(4), 911; https://doi.org/10.3390/en17040911 - 15 Feb 2024
Cited by 2 | Viewed by 1256
Abstract
The paper presents a novel approach for predicting battery energy consumption in electric city buses (e-buses) by means of a trip-based data-driven regression model. The model was parameterized based on the data collected by running a physical experimentally validated e-bus simulation model, and [...] Read more.
The paper presents a novel approach for predicting battery energy consumption in electric city buses (e-buses) by means of a trip-based data-driven regression model. The model was parameterized based on the data collected by running a physical experimentally validated e-bus simulation model, and it consists of powertrain and heating, ventilation, and air conditioning (HVAC) system submodels. The main advantage of the proposed approach is its reliance on readily available trip-related data, such as travel distance, mean velocity, average passenger count, mean and standard deviation of road slope, and mean ambient temperature and solar irradiance, as opposed to the physical model, which requires high-sampling-rate driving cycle data. Additionally, the data-driven model is executed significantly faster than the physical model, thus making it suitable for large-scale city bus electrification planning or online energy consumption prediction applications. The data-driven model development began with applying feature selection techniques to identify the most relevant set of model inputs. Machine learning methods were then employed to achieve a model that effectively balances accuracy, simplicity, and interpretability. The validation results of the final eight-input quadratic-form e-bus model demonstrated its high precision and generalization, which was reflected in the R2 value of 0.981 when tested on unseen data. Owing to the trip-based, mean-value formulation, the model executed six orders of magnitude faster than the physical model. Full article
(This article belongs to the Special Issue Application of AI in Energy Savings and CO2 Reduction)
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12 pages, 656 KiB  
Article
Data-Driven Modeling of Appliance Energy Usage
by Cameron Francis Assadian and Francis Assadian
Energies 2023, 16(22), 7536; https://doi.org/10.3390/en16227536 - 12 Nov 2023
Viewed by 1708
Abstract
Due to the transition toward the Internet of Everything (IOE), the prediction of energy consumed by household appliances has become a progressively more difficult topic to model. Even with advancements in data analytics and machine learning, several challenges remain to be addressed. Therefore, [...] Read more.
Due to the transition toward the Internet of Everything (IOE), the prediction of energy consumed by household appliances has become a progressively more difficult topic to model. Even with advancements in data analytics and machine learning, several challenges remain to be addressed. Therefore, providing highly accurate and optimized models has become the primary research goal of many studies. This paper analyzes appliance energy consumption through a variety of machine learning-based strategies. Utilizing data recorded from a single-family home, input variables comprised internal temperatures and humidities, lighting consumption, and outdoor conditions including wind speed, visibility, and pressure. Various models were trained and evaluated: (a) multiple linear regression, (b) support vector regression, (c) random forest, (d) gradient boosting, (e) xgboost, and (f) the extra trees regressor. Both feature engineering and hyperparameter tuning methodologies were applied to not only extend existing features but also create new ones that provided improved model performance across all metrics: root mean square error (RMSE), coefficient of determination (R2), mean absolute error (MAE), and mean absolute percentage error (MAPE). The best model (extra trees) was able to explain 99% of the variance in the training set and 66% in the testing set when using all the predictors. The results were compared with those obtained using a similar methodology. The objective of performing these actions was to show a unique perspective in simulating building performance through data-driven models, identifying how to maximize predictive performance through the use of machine learning-based strategies, as well as understanding the potential benefits of utilizing different models. Full article
(This article belongs to the Special Issue Application of AI in Energy Savings and CO2 Reduction)
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