scholarly journals State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1301 ◽  
Author(s):  
Amir Mosavi ◽  
Mohsen Salimi ◽  
Sina Faizollahzadeh Ardabili ◽  
Timon Rabczuk ◽  
Shahaboddin Shamshirband ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Demand ◽  
Prediction Models ◽  
State Of The Art ◽  
Energy Systems ◽  
Demand Prediction ◽  
Energy Governance ◽  
Challenges And Opportunities ◽  
And Performance ◽  
Energy Type

Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. Through a novel methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and application area. Furthermore, a comprehensive review of the literature leads to an assessment and performance evaluation of the ML models and their applications, and a discussion of the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models. Hybridization is reported to be effective in the advancement of prediction models, particularly for renewable energy systems, e.g., solar energy, wind energy, and biofuels. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability.

scholarly journals Demand Prediction with Machine Learning Models; State of the Art and a Systematic Review of Advances

2019 ◽  
Author(s):  
Amir Mosavi ◽  
Sina Faizollahzadeh Ardabili ◽  
Shahabodin Shamshirband
Keyword(s):  
Machine Learning ◽  
Energy Production ◽  
Production Management ◽  
Prediction Models ◽  
State Of The Art ◽  
Electricity Demand ◽  
Original Research ◽  
Review Of The Literature ◽  
Future Prospects ◽  
Demand Prediction

Electricity demand prediction is vital for energy production management and proper exploitation of the present resources. Recently, several novel machine learning (ML) models have been employed for electricity demand prediction to estimate the future prospects of the energy requirements. The main objective of this study is to review the various ML models applied for electricity demand prediction. Through a novel search and taxonomy, the most relevant original research articles in the field are identified and further classified according to the ML modeling technique, perdition type, and the application area. A comprehensive review of the literature identifies the major ML models, their applications and a discussion on the evaluation of their performance. This paper further makes a discussion on the trend and the performance of the ML models. As the result, this research reports an outstanding rise in the accuracy, robustness, precision and the generalization ability of the prediction models using the hybrid and ensemble ML algorithms.

scholarly journals Using Real-Time Electricity Prices to Leverage Electrical Energy Storage and Flexible Loads in a Smart Grid Environment Utilizing Machine Learning Techniques

Processes ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. 870 ◽  
Author(s):  
Moataz Sheha ◽  
Kody Powell
Keyword(s):  
Machine Learning ◽  
Energy Storage ◽  
Energy Demand ◽  
Prediction Models ◽  
Electrical Energy ◽  
Machine Learning Techniques ◽  
Electrical Energy Storage ◽  
Demand Prediction ◽  
Learning Techniques ◽  
Flexible Loads

With exposure to real-time market pricing structures, consumers would be incentivized to invest in electrical energy storage systems and smart predictive automation of their home energy systems. Smart home automation through optimizing HVAC (heating, ventilation, and air conditioning) temperature set points, along with distributed energy storage, could be utilized in the process of optimizing the operation of the electric grid. Using electricity prices as decision variables to leverage electrical energy storage and flexible loads can be a valuable tool to optimize the performance of the power grid and reduce electricity costs both on the supply and demand sides. Energy demand prediction is important for proper allocation and utilization of the available resources. Manipulating energy prices to leverage storage and flexible loads through these demand prediction models is a novel idea that needs to be studied. In this paper, different models for proactive prediction of the energy demand for an entire city using different machine learning techniques are presented and compared. The results of the machine learning techniques show that the proposed nonlinear autoregressive with exogenous inputs neural network model resulted in the most accurate predictions. These prediction models pave the way for the demand side to become an important asset for grid regulation by responding to variable price signals through battery energy storage and passive thermal energy storage using HVAC temperature set points.

scholarly journals Machine Learning for Benchmarking Models of Heating Energy Demand of Houses in Northern Canada

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1158
Author(s):  
Behrad Bezyan ◽  
Radu Zmeureanu
Keyword(s):  
Machine Learning ◽  
Energy Demand ◽  
Energy Use ◽  
Machine Learning Techniques ◽  
Northern Canada ◽  
Automation And Control ◽  
And Performance ◽  
Energy Signature ◽  
Heating Energy Demand ◽  
And Control

In most cases, the benchmarking models of energy use in houses are developed based on current and past data, and they continue to be used without any update. This paper proposes the method of retraining of benchmarking models by applying machine learning techniques when new measurements are made available. The method uses as a case study the measurements of heating energy demand from two semi-detached houses of Northern Canada. The results of the prediction of heating energy demand using static or augmented window techniques are compared with measurements. The daily energy signature is used as a benchmarking model due to its simplicity and performance. However, the proposed retraining method can be applied to any form of benchmarking model. The method should be applied in all possible situations, and be an integral part of intelligent building automation and control systems (BACS) for the ongoing commissioning for building energy-related applications.

Machine learning-based thermal response time ahead energy demand prediction for building heating systems

2018 ◽  
Vol 221 ◽  
pp. 16-27 ◽  
Author(s):  
Yabin Guo ◽  
Jiangyu Wang ◽  
Huanxin Chen ◽  
Guannan Li ◽  
Jiangyan Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Response Time ◽  
Energy Demand ◽  
Thermal Response ◽  
Heating Systems ◽  
Demand Prediction ◽  
Thermal Response Time ◽  
Energy Demand Prediction

scholarly journals Machine learning for energy-water nexus: challenges and opportunities

2018 ◽  
Vol 2 (3) ◽  
pp. 228-267 ◽  
Author(s):  
Zaidi ◽  
Chandola ◽  
Allen ◽  
Sanyal ◽  
Stewart ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Systems ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Technological Advancement ◽  
Research Directions ◽  
Learning Techniques ◽  
Challenges And Opportunities ◽  
Future Research Directions ◽  
System Sustainability

Modeling the interactions of water and energy systems is important to the enforcement of infrastructure security and system sustainability. To this end, recent technological advancement has allowed the production of large volumes of data associated with functioning of these sectors. We are beginning to see that statistical and machine learning techniques can help elucidate characteristic patterns across these systems from water availability, transport, and use to energy generation, fuel supply, and customer demand, and in the interdependencies among these systems that can leave these systems vulnerable to cascading impacts from single disruptions. In this paper, we discuss ways in which data and machine learning can be applied to the challenges facing the energy-water nexus along with the potential issues associated with the machine learning techniques themselves. We then survey machine learning techniques that have found application to date in energy-water nexus problems. We conclude by outlining future research directions and opportunities for collaboration among the energy-water nexus and machine learning communities that can lead to mutual synergistic advantage.

scholarly journals Effectiveness, Explainability and Reliability of Machine Meta-Learning Methods for Predicting Mortality in Patients with COVID-19: Results of the Brazilian COVID-19 Registry

2021 ◽  
Author(s):  
Bruno Barbosa Miranda de Paiva ◽  
Polianna Delfino Pereira ◽  
Claudio Moises Valiense de Andrade ◽  
Virginia Mara Reis Gomes ◽  
Maria Clara Pontello Barbosa Lima ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
State Of The Art ◽  
Laboratory Data ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Learning Models ◽  
Learning Methods ◽  
Meta Learning ◽  
Machine Learning Models

Objective: To provide a thorough comparative study among state ofthe art machine learning methods and statistical methods for determining in-hospital mortality in COVID 19 patients using data upon hospital admission; to study the reliability of the predictions of the most effective methods by correlating the probability of the outcome and the accuracy of the methods; to investigate how explainable are the predictions produced by the most effective methods. Materials and Methods: De-identified data were obtained from COVID 19 positive patients in 36 participating hospitals, from March 1 to September 30, 2020. Demographic, comorbidity, clinical presentation and laboratory data were used as training data to develop COVID 19 mortality prediction models. Multiple machine learning and traditional statistics models were trained on this prediction task using a folded cross validation procedure, from which we assessed performance and interpretability metrics. Results: The Stacking of machine learning models improved over the previous state of the art results by more than 26% in predicting the class of interest (death), achieving 87.1% of AUROC and macroF1 of 73.9%. We also show that some machine learning models can be very interpretable and reliable, yielding more accurate predictions while providing a good explanation for the why. Conclusion: The best results were obtained using the meta learning ensemble model Stacking. State of the art explainability techniques such as SHAP values can be used to draw useful insights into the patterns learned by machine-learning algorithms. Machine learning models can be more explainable than traditional statistics models while also yielding highly reliable predictions. Key words: COVID-19; prognosis; prediction model; machine learning

scholarly journals Evaluation of crowdsourced mortality prediction models as a framework for assessing AI in medicine

2021 ◽  
Author(s):  
Timothy Bergquist ◽  
Thomas Schaffter ◽  
Yao Yan ◽  
Thomas Yu ◽  
Justin Prosser ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Improve Patient Care ◽  
Mortality Prediction ◽  
Direct Access ◽  
Healthcare Outcomes ◽  
Patient Privacy ◽  
Mortality Prediction Models ◽  
And Performance ◽  
Similar Accuracy

AbstractApplications of machine learning in healthcare are of high interest and have the potential to significantly improve patient care. Yet, the real-world accuracy and performance of these models on different patient subpopulations remains unclear. To address these important questions, we hosted a community challenge to evaluate different methods that predict healthcare outcomes. To overcome patient privacy concerns, we employed a Model-to-Data approach, allowing citizen scientists and researchers to train and evaluate machine learning models on private health data without direct access to that data. We focused on the prediction of all-cause mortality as the community challenge question. In total, we had 345 registered participants, coalescing into 25 independent teams, spread over 3 continents and 10 countries. The top performing team achieved a final area under the receiver operator curve of 0.947 (95% CI 0.942, 0.951) and an area under the precision-recall curve of 0.487 (95% CI 0.458, 0.499) on patients prospectively collected over a one year observation of a large health system. Post-hoc analysis after the challenge revealed that models differ in accuracy on subpopulations, delineated by race or gender, even when they are trained on the same data and have similar accuracy on the population. This is the largest community challenge focused on the evaluation of state-of-the-art machine learning methods in a healthcare system performed to date, revealing both opportunities and pitfalls of clinical AI.

scholarly journals A review on machine learning for neutrino experiments

2020 ◽  
Vol 35 (33) ◽  
pp. 2043005
Author(s):  
Fernanda Psihas ◽  
Micah Groh ◽  
Christopher Tunnell ◽  
Karl Warburton
Keyword(s):  
Machine Learning ◽  
Neutrino Physics ◽  
State Of The Art ◽  
Machine Learning Algorithms ◽  
Current Status ◽  
Learning Tools ◽  
The Standard Model ◽  
Challenges And Opportunities ◽  
Machine Learning Applications ◽  
Neutrino Experiments

Neutrino experiments study the least understood of the Standard Model particles by observing their direct interactions with matter or searching for ultra-rare signals. The study of neutrinos typically requires overcoming large backgrounds, elusive signals, and small statistics. The introduction of state-of-the-art machine learning tools to solve analysis tasks has made major impacts to these challenges in neutrino experiments across the board. Machine learning algorithms have become an integral tool of neutrino physics, and their development is of great importance to the capabilities of next generation experiments. An understanding of the roadblocks, both human and computational, and the challenges that still exist in the application of these techniques is critical to their proper and beneficial utilization for physics applications. This review presents the current status of machine learning applications for neutrino physics in terms of the challenges and opportunities that are at the intersection between these two fields.

Bridging Finite Element and Machine Learning Modeling: Stress Prediction of Arterial Walls in Atherosclerosis

2019 ◽  
Vol 141 (8) ◽  
Author(s):  
Ali Madani ◽  
Ahmed Bakhaty ◽  
Jiwon Kim ◽  
Yara Mubarak ◽  
Mohammad R. K. Mofrad
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Finite Element ◽  
Deep Neural Networks ◽  
Prediction Models ◽  
Plaque Rupture ◽  
Training Data ◽  
Von Mises Stress ◽  
And Performance ◽  
Von Mises

Finite element and machine learning modeling are two predictive paradigms that have rarely been bridged. In this study, we develop a parametric model to generate arterial geometries and accumulate a database of 12,172 2D finite element simulations modeling the hyperelastic behavior and resulting stress distribution. The arterial wall composition mimics vessels in atherosclerosis–a complex cardiovascular disease and one of the leading causes of death globally. We formulate the training data to predict the maximum von Mises stress, which could indicate risk of plaque rupture. Trained deep learning models are able to accurately predict the max von Mises stress within 9.86% error on a held-out test set. The deep neural networks outperform alternative prediction models and performance scales with amount of training data. Lastly, we examine the importance of contributing features on stress value and location prediction to gain intuitions on the underlying process. Moreover, deep neural networks can capture the functional mapping described by the finite element method, which has far-reaching implications for real-time and multiscale prediction tasks in biomechanics.

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