Guest Editors
Ali Khosravi, Aalto University, Finland
Timo Laukkanen, Aalto University, Finland
Mohammad Malekan, Aarhus University, Denmark
Mamdouh El Haj Assad, University of Sharjah, UAE
Juan José Garcia Pabon, University of Itajuba, Brazil
Summary
In recent years, Artificial Intelligence (AI) has gained relevance in a wide variety of sectors. Artificial Intelligence becomes more and more important in the energy engineering and is having great potential for the future design of the energy and renewable energy system. Typical areas of application are electricity market, smart grids, or the sector coupling of electricity, district heating and cooling networks and transport. Prerequisites for an increased use of AI in the energy system are the digitalization of the energy sector and a correspondingly large set of data that is evaluable. AI helps make the energy industry more efficient and secure by analyzing and evaluating the data volumes. The main objective and contribution of this special issue is to present how AI techniques might play an important role in modeling and optimizing the energy systems. The special issue outlines an understanding of how expert systems and neural networks operate by way of presenting a number of problems in the different disciplines of energy engineering. The scope of the Special Issue is application of AI for:
Renewable Energy Systems: Solar Energy; Wind Energy; Marine Energy; Ocean Thermal; Geothermal; Biomass; Nuclear Energy; Hydroelectricity;
Energy Systems: Electricity Network; District Heating Network; District Cooling Network;
Energy Storage: Thermal Energy Storage (Sensible Heat; Latent Heat; Seasonal Energy Storage); Electricity Storage (Hydrogen Fuel Cell, Battery, CAES, Gravity Energy Storage);
Heat Pumps: Compression Cycles; Absorption Systems; Clean Fuels
Keywords
Artificial Intelligence; Machine learning; Renewable energy systems; Energy Storage; Electricity market; District heating network; District cooling network; Clean Fuels; Power-to-X
Published Papers
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Open Access
ARTICLE
Classification of Transmission Line Ground Short Circuit Fault Based on Convolutional Neural Network
Tao Guo, Gang Tian, Zhimin Ao, Xi Fang, Lili Wei, Fei Li
Energy Engineering, Vol.119, No.3, pp. 985-996, 2022, DOI:10.32604/ee.2022.018185
（This article belongs to this Special Issue:
The Role of Artificial Intelligence for Modeling and Optimizing the Energy Systems )
Abstract Ground short circuit faults in current transmission lines are common in the power systems. In order to prevent the power system from aggravating the accident caused by short-circuit faults of transmission lines, a novel convolutional neural network (CNN) model is constructed to identify the short-circuit fault of the transmission line in the power system. The CNN model is mainly consisted of five convolutional layers, three max-pooling layers, one concatenate layer, one dropout layer, one fully connected layer, and a Softmax classifier. This method uses a fixed time window to intercept system short-circuit fault data, extracts the deep features of these…
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Open Access
ARTICLE
Research on Flashover Voltage Prediction of Catenary Insulator Based on CaSO4 Pollution with Different Mass Fraction
Sihua Wang, Junjun Wang, Lijun Zhou, Long Chen, Lei Zhao
Energy Engineering, Vol.119, No.1, pp. 219-236, 2022, DOI:10.32604/EE.2022.016899
（This article belongs to this Special Issue:
The Role of Artificial Intelligence for Modeling and Optimizing the Energy Systems )
Abstract Pollution flashover accidents occur frequently in railway OCS in saline-alkali areas. To accurately predict the pollution flashover voltage of insulators, a pollution flashover warning should be made in advance. According to the operating environment of insulators along the Qinghai-Tibet railway, the pollution flashover experiments were designed for the cantilever composite insulator FQBG-25/12. Through the experiments, the flashover voltage under the influence of soluble contaminant density (SCD) of different pollution components, non-soluble deposit density (NSDD), temperature (T), and atmospheric pressure (P) was obtained. On this basis, the GA-BP neural network prediction model was established. P, SCD, NSDD, CaSO4 mass fraction (w(CaSO4)),… More >
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Open Access
ARTICLE
Comprehensive Study, Design and Economic Feasibility Analysis of Solar PV Powered Water Pumping System
K. Karthick, K. Jaiganesh, S. Kavaskar
Energy Engineering, Vol.118, No.6, pp. 1887-1904, 2021, DOI:10.32604/EE.2021.017563
（This article belongs to this Special Issue:
The Role of Artificial Intelligence for Modeling and Optimizing the Energy Systems )
Abstract The energy efficient product can be operated with longer duration. They offer wonderful solutions compared to other conventional water pumping system as it needs less maintenance, simple in installation, zero fuel cost, longer operating life, highly reliable and free from production of greenhouse gases. In this paper we analyzed the different topologies of DC–DC converter in terms of their operating region of MPPT, quality of input and output currents. We discussed the MPPT algorithms to address partial shading effects in SPV array, present state of the technology, factors affecting the performance of the system, efficiency improvements and identified the research…
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Open Access
ARTICLE
Reliability Based Multi-Objective Thermodynamic Cycle Optimisation of Turbofan Engines Using Luus-Jaakola Algorithm
Vin Cent Tai, Yong Chai Tan, Nor Faiza Abd Rahman, Yaw Yoong Sia, Chan Chin Wang, Lip Huat Saw
Energy Engineering, Vol.118, No.4, pp. 1057-1068, 2021, DOI:10.32604/EE.2021.014866
（This article belongs to this Special Issue:
The Role of Artificial Intelligence for Modeling and Optimizing the Energy Systems )
Abstract Aircraft engine design is a complicated process, as it involves huge number of components. The design process begins with parametric cycle analysis. It is crucial to determine the optimum values of the cycle parameters that would give a robust design in the early phase of engine development, to shorten the design cycle for cost saving and man-hour reduction. To obtain a robust solution, optimisation program is often being executed more than once, especially in Reliability Based Design Optimisations (RBDO) with Monte-Carlo Simulation (MCS) scheme for complex systems which require thousands to millions of optimisation loops to be executed. This paper…
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Open Access
ARTICLE
A Novel Power Curve Prediction Method for Horizontal-Axis Wind Turbines Using Artificial Neural Networks
Vin Cent Tai, Yong Chai Tan, Nor Faiza Abd Rahman, Chee Ming Chia, Mirzhakyp Zhakiya, Lip Huat Saw
Energy Engineering, Vol.118, No.3, pp. 507-516, 2021, DOI:10.32604/EE.2021.014868
（This article belongs to this Special Issue:
The Role of Artificial Intelligence for Modeling and Optimizing the Energy Systems )
Abstract Accurate prediction of wind turbine power curve is essential for wind farm planning as it influences the expected power production. Existing methods require detailed wind turbine geometry for performance evaluation, which most of the time unattainable and impractical in early stage of wind farm planning. While significant amount of work has been done on fitting of wind turbine power curve using parametric and non-parametric models, little to no attention has been paid for power curve modelling that relates the wind turbine design information. This paper presents a novel method that employs artificial neural network to learn the underlying relationships between…
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Open Access
ARTICLE
Long-Term Electricity Demand Forecasting for Malaysia Using Artificial Neural Networks in the Presence of Input and Model Uncertainties
Vin Cent Tai, Yong Chai Tan, Nor Faiza Abd Rahman, Hui Xin Che, Chee Ming Chia, Lip Huat Saw, Mohd Fozi Ali
Energy Engineering, Vol.118, No.3, pp. 715-725, 2021, DOI:10.32604/EE.2021.014865
（This article belongs to this Special Issue:
The Role of Artificial Intelligence for Modeling and Optimizing the Energy Systems )
Abstract Electricity demand is also known as load in electric power system. This article presents a Long-Term Load Forecasting (LTLF) approach for Malaysia. An Artificial Neural Network (ANN) of 5-layer Multi-Layered Perceptron (MLP) structure has been designed and tested for this purpose. Uncertainties of input variables and ANN model were introduced to obtain the prediction for years 2022 to 2030. Pearson correlation was used to examine the input variables for model construction. The analysis indicates that Primary Energy Supply (PES), population, Gross Domestic Product (GDP) and temperature are strongly correlated. The forecast results by the proposed method (henceforth referred to as…
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