Intelligent Energy Demand Forecasting

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Intelligent Energy Demand Forecasting

Author : Wei-Chiang Hong
Publisher : Springer Science & Business Media
ISBN 13 : 1447149688
Total Pages : 203 pages
Book Rating : 4.4/5 (471 download)

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Book Synopsis Intelligent Energy Demand Forecasting by : Wei-Chiang Hong

Download or read book Intelligent Energy Demand Forecasting written by Wei-Chiang Hong and published by Springer Science & Business Media. This book was released on 2013-03-12 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: As industrial, commercial, and residential demands increase and with the rise of privatization and deregulation of the electric energy industry around the world, it is necessary to improve the performance of electric operational management. Intelligent Energy Demand Forecasting offers approaches and methods to calculate optimal electric energy allocation to reach equilibrium of the supply and demand. Evolutionary algorithms and intelligent analytical tools to improve energy demand forecasting accuracy are explored and explained in relation to existing methods. To provide clearer picture of how these hybridized evolutionary algorithms and intelligent analytical tools are processed, Intelligent Energy Demand Forecasting emphasizes on improving the drawbacks of existing algorithms. Written for researchers, postgraduates, and lecturers, Intelligent Energy Demand Forecasting helps to develop the skills and methods to provide more accurate energy demand forecasting by employing novel hybridized evolutionary algorithms and intelligent analytical tools.

Hybrid Intelligent Technologies in Energy Demand Forecasting

Author : Wei-Chiang Hong
Publisher : Springer Nature
ISBN 13 : 3030365298
Total Pages : 179 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Hybrid Intelligent Technologies in Energy Demand Forecasting by : Wei-Chiang Hong

Download or read book Hybrid Intelligent Technologies in Energy Demand Forecasting written by Wei-Chiang Hong and published by Springer Nature. This book was released on 2020-01-01 with total page 179 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is written for researchers and postgraduates who are interested in developing high-accurate energy demand forecasting models that outperform traditional models by hybridizing intelligent technologies. It covers meta-heuristic algorithms, chaotic mapping mechanism, quantum computing mechanism, recurrent mechanisms, phase space reconstruction, and recurrence plot theory. The book clearly illustrates how these intelligent technologies could be hybridized with those traditional forecasting models. This book provides many figures to deonstrate how these hybrid intelligent technologies are being applied to exceed the limitations of existing models.

Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast

Author : Federico Divina
Publisher : MDPI
ISBN 13 : 3036508627
Total Pages : 100 pages
Book Rating : 4.0/5 (365 download)

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Book Synopsis Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast by : Federico Divina

Download or read book Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast written by Federico Divina and published by MDPI. This book was released on 2021-08-30 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of data collectors in energy systems is growing more and more. For example, smart sensors are now widely used in energy production and energy consumption systems. This implies that huge amounts of data are generated and need to be analyzed in order to extract useful insights from them. Such big data give rise to a number of opportunities and challenges for informed decision making. In recent years, researchers have been working very actively in order to come up with effective and powerful techniques in order to deal with the huge amount of data available. Such approaches can be used in the context of energy production and consumption considering the amount of data produced by all samples and measurements, as well as including many additional features. With them, automated machine learning methods for extracting relevant patterns, high-performance computing, or data visualization are being successfully applied to energy demand forecasting.

Applications of Big Data and Artificial Intelligence in Smart Energy Systems

Author : Neelu Nagpal
Publisher : CRC Press
ISBN 13 : 1000963829
Total Pages : 318 pages
Book Rating : 4.0/5 (9 download)

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Book Synopsis Applications of Big Data and Artificial Intelligence in Smart Energy Systems by : Neelu Nagpal

Download or read book Applications of Big Data and Artificial Intelligence in Smart Energy Systems written by Neelu Nagpal and published by CRC Press. This book was released on 2023-09-29 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the era of propelling traditional energy systems to evolve towards smart energy systems, including power generation, energy storage systems, and electricity consumption have become more dynamic. The quality and reliability of power supply are impacted by the sporadic and rising use of electric vehicles, domestic loads, and industrial loads. Similarly, with the integration of solid state devices, renewable sources, and distributed generation, power generation processes are evolving in a variety of ways. Several cutting-edge technologies are necessary for the safe and secure operation of power systems in such a dynamic setting, including load distribution, automation, energy regulation & control, and energy trading. This book covers the applications of various big data analytics,artificial intelligence, and machine learning technologies in smart grids for demand prediction, decision-making processes, policy, and energy management. The book delves into the new technologies for modern power systems such as the Internet of Things, Blockchain for smart home and smart city solutions in depth. Technical topics discussed in the book include: • Hybrid smart energy system technologies • Smart meters • Energy demand forecasting • Use of different protocols and communication in smart energy systems • Power quality and allied issues and mitigation using AI • Intelligent transportation • Virtual power plants • AI based smart energy business models • Smart home solutions • Blockchain solutions for smart grids.

Intelligent Optimization Modelling in Energy Forecasting

Author : Wei-Chiang Hong
Publisher : MDPI
ISBN 13 : 3039283642
Total Pages : 262 pages
Book Rating : 4.0/5 (392 download)

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Book Synopsis Intelligent Optimization Modelling in Energy Forecasting by : Wei-Chiang Hong

Download or read book Intelligent Optimization Modelling in Energy Forecasting written by Wei-Chiang Hong and published by MDPI. This book was released on 2020-04-01 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accurate energy forecasting is important to facilitate the decision-making process in order to achieve higher efficiency and reliability in power system operation and security, economic energy use, contingency scheduling, the planning and maintenance of energy supply systems, and so on. In recent decades, many energy forecasting models have been continuously proposed to improve forecasting accuracy, including traditional statistical models (e.g., ARIMA, SARIMA, ARMAX, multi-variate regression, exponential smoothing models, Kalman filtering, Bayesian estimation models, etc.) and artificial intelligence models (e.g., artificial neural networks (ANNs), knowledge-based expert systems, evolutionary computation models, support vector regression, etc.). Recently, due to the great development of optimization modeling methods (e.g., quadratic programming method, differential empirical mode method, evolutionary algorithms, meta-heuristic algorithms, etc.) and intelligent computing mechanisms (e.g., quantum computing, chaotic mapping, cloud mapping, seasonal mechanism, etc.), many novel hybrid models or models combined with the above-mentioned intelligent-optimization-based models have also been proposed to achieve satisfactory forecasting accuracy levels. It is important to explore the tendency and development of intelligent-optimization-based modeling methodologies and to enrich their practical performances, particularly for marine renewable energy forecasting.

Applications of Big Data and Artificial Intelligence in Smart Energy Systems

Author : Neelu Nagpal
Publisher :
ISBN 13 : 9788770228275
Total Pages : 0 pages
Book Rating : 4.2/5 (282 download)

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Book Synopsis Applications of Big Data and Artificial Intelligence in Smart Energy Systems by : Neelu Nagpal

Download or read book Applications of Big Data and Artificial Intelligence in Smart Energy Systems written by Neelu Nagpal and published by . This book was released on 2023-08-31 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers smart grid applications of various big data analytics, artificial intelligence, and machine learning technologies for demand prediction, decision-making processes, policy, and energy management. It delves into the new technologies such as the Internet of Things, blockchain, etc. for smart home solutions, and smart city solutions in depth in the context of the modern power systems. In the era of propelling traditional energy systems to evolve towards smart energy systems, systems, including power generation energy storage systems, and electricity consumption have become more dynamic. The quality and reliability of power supply are impacted by the sporadic and rising use of electric vehicles, and domestic and industrial loads. Similarly, with the integration of solid-state devices, renewable sources, and distributed generation, power generation processes are evolving in a variety of ways. Several cutting-edge technologies are necessary for the safe and secure operation of power systems in such a dynamic setting, including load distribution automation, energy regulation and control, and energy trading. Technical topics discussed in the book include: Hybrid smart energy system technologies Energy demand forecasting Use of different protocols and communication in smart energy systems Power quality and allied issues and mitigation using AI Intelligent transportation Virtual power plants AI business models

Energy Demand Forecasting in Smart Buildings

Author : Álvaro Picatoste Ruilope
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (112 download)

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Book Synopsis Energy Demand Forecasting in Smart Buildings by : Álvaro Picatoste Ruilope

Download or read book Energy Demand Forecasting in Smart Buildings written by Álvaro Picatoste Ruilope and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Energy demand forecasting has become a relevant subject in the energy management field. Different techniques are being currently applied to forecast the energy demand for different time horizons and for diverse types of loads. Some of them are based in complex Machine Learning (ML) algorithms, which maps the energy consumption to a set of influence parameters or inputs, such as the historical data consumption, the weather or other variables, making it possible to predict the energy demand. Important management decisions from different stakeholders in the Energy sector are based on these predictions and, therefore, it is important to rigorously assess the performance of these predictive models. A specific methodology is presented in this dissertation through its application over a real-building case-study in which energy demand predictions are being carried out by a ML model. All the steps in the evaluation process are explained and exemplified, including the data gathering, evaluation period selection, data preprocess with special emphasis in the data abnormalities an its relation to the process dynamics and, finally, the data process itself. The accuracy of the model and the main parameters of influence are evaluated through four different metrics and data visualizations, based mainly in box-andwhisker plots. Several anomalies when predicting energy consumption in a disaggregated load (single building) have been found in the study. By removing them the stability of the case-study model is around 88%. The metrics yield a MAPE (Mean Absolute Percentage Error) of 18.05% and a MBPE (Mean Biased Percentage Error) of -4.67%. While being values within the literature range they show a poor accuracy. Nevertheless, there is space for improvement and by retraining, refining and calibrating the model it will be possible to improve its performance. The day of the week, the working calendar and the hour of the day showed to have a strong influence over the error metrics analyzed. Other alernative Machine Learnings methodologies have been applied to the same dataset and their performance have been analyzed. Artificial Neural Network, k-Nearest Neighbors and Random Forest based models have been compared after training with more than 1-year hourly Energy Consumption data and other influence variables. The Random Forest achieved the best accuracy when re-trained, showing a MAPE below 10%. The importance of passing a detailed working calendar to the model, using accurate weather variables forecasts and defining an adequate re-training strategy have been proved to improve model accuracy.

Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast

Author : Francisco A. Gómez Vela
Publisher :
ISBN 13 : 9783036508634
Total Pages : 100 pages
Book Rating : 4.5/5 (86 download)

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Book Synopsis Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast by : Francisco A. Gómez Vela

Download or read book Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast written by Francisco A. Gómez Vela and published by . This book was released on 2021 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of data collectors in energy systems is growing more and more. For example, smart sensors are now widely used in energy production and energy consumption systems. This implies that huge amounts of data are generated and need to be analyzed in order to extract useful insights from them. Such big data give rise to a number of opportunities and challenges for informed decision making. In recent years, researchers have been working very actively in order to come up with effective and powerful techniques in order to deal with the huge amount of data available. Such approaches can be used in the context of energy production and consumption considering the amount of data produced by all samples and measurements, as well as including many additional features. With them, automated machine learning methods for extracting relevant patterns, high-performance computing, or data visualization are being successfully applied to energy demand forecasting. In light of the above, this Special Issue collects the latest research on relevant topics, in particular in energy demand forecasts, and the use of advanced optimization methods and big data techniques. Here, by energy, we mean any kind of energy, e.g., electrical, solar, microwave, or wind.

Applications of Big Data and Artificial Intelligence in Smart Energy Systems

Author : Neetika Kaushal Nagpal
Publisher :
ISBN 13 : 9781003440864
Total Pages : 0 pages
Book Rating : 4.4/5 (48 download)

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Book Synopsis Applications of Big Data and Artificial Intelligence in Smart Energy Systems by : Neetika Kaushal Nagpal

Download or read book Applications of Big Data and Artificial Intelligence in Smart Energy Systems written by Neetika Kaushal Nagpal and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the era of propelling traditional energy systems to evolve towards smart energy systems, systems, including power generation energy storage systems, and electricity consumption have become more dynamic. The quality and reliability of power supply are impacted by the sporadic and rising use of electric vehicles, and domestic and industrial loads. Similarly, with the integration of solid state devices, renewable sources, and distributed generation, power generation processes are evolving in a variety of ways. Several cutting-edge technologies are necessary for the safe and secure operation of power systems in such a dynamic setting, including load distribution automation, energy regulation and control, and energy trading. This book covers the applications of various big data analytics, artificial intelligence, and machine learning technologies in smart grids for demand prediction, decision-making processes, policy, and energy management. The book delves into the new technologies such as the Internet of Things, blockchain, etc. for smart home solutions, and smart city solutions in depth in the context of the modern power systems. Technical topics discussed in the book include: • Hybrid smart energy system technologies • Energy demand forecasting • Use of different protocols and communication in smart energy systems • Power quality and allied issues and mitigation using AI • Intelligent transportation • Virtual power plants • AI business models.

Prediction Techniques for Renewable Energy Generation and Load Demand Forecasting

Author : Anuradha Tomar
Publisher : Springer Nature
ISBN 13 : 9811964904
Total Pages : 208 pages
Book Rating : 4.8/5 (119 download)

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Book Synopsis Prediction Techniques for Renewable Energy Generation and Load Demand Forecasting by : Anuradha Tomar

Download or read book Prediction Techniques for Renewable Energy Generation and Load Demand Forecasting written by Anuradha Tomar and published by Springer Nature. This book was released on 2023-01-20 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an introduction to forecasting methods for renewable energy sources integrated with existing grid. It consists of two sections; the first one is on the generation side forecasting methods, while the second section deals with the different ways of load forecasting. It broadly includes artificial intelligence, machine learning, hybrid techniques and other state-of-the-art techniques for renewable energy and load predictions. The book reflects the state of the art in distributed generation system and future microgrids and covers theory, algorithms, simulations and case studies. It offers invaluable insights through this valuable resource to students and researchers working in the fields of renewable energy, integration of renewable energy with existing grid and electrical distribution network.

Forecasting and Assessing Risk of Individual Electricity Peaks

Author : Maria Jacob
Publisher : Springer Nature
ISBN 13 : 303028669X
Total Pages : 108 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Forecasting and Assessing Risk of Individual Electricity Peaks by : Maria Jacob

Download or read book Forecasting and Assessing Risk of Individual Electricity Peaks written by Maria Jacob and published by Springer Nature. This book was released on 2019-09-25 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: The overarching aim of this open access book is to present self-contained theory and algorithms for investigation and prediction of electric demand peaks. A cross-section of popular demand forecasting algorithms from statistics, machine learning and mathematics is presented, followed by extreme value theory techniques with examples. In order to achieve carbon targets, good forecasts of peaks are essential. For instance, shifting demand or charging battery depends on correct demand predictions in time. Majority of forecasting algorithms historically were focused on average load prediction. In order to model the peaks, methods from extreme value theory are applied. This allows us to study extremes without making any assumption on the central parts of demand distribution and to predict beyond the range of available data. While applied on individual loads, the techniques described in this book can be extended naturally to substations, or to commercial settings. Extreme value theory techniques presented can be also used across other disciplines, for example for predicting heavy rainfalls, wind speed, solar radiation and extreme weather events. The book is intended for students, academics, engineers and professionals that are interested in short term load prediction, energy data analytics, battery control, demand side response and data science in general.

Scalable Local Short-term Energy Consumption Forecasting

Author : Jay D Buckler
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (14 download)

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Book Synopsis Scalable Local Short-term Energy Consumption Forecasting by : Jay D Buckler

Download or read book Scalable Local Short-term Energy Consumption Forecasting written by Jay D Buckler and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Smart meter adoption rates are increasing globally and this has contributed to a rapid increase in the type and volume of data: communication, storage, and processing. These recent advances have created new opportunities for smart grid research, particularly in developing effective methods for processing big data. As power system industry moves towards adapting and implementing smart grid functions, energy demands forecasting is mandated at the distribution level to ensure the balance between energy supply and demand. Unlike system-level forecasting, short term energy demand forecasting at the distribution level needs to be highly scalable, due to the needs for collecting and processing energy demand data for a significant number of loads over a short time. This scalability requirement is magnified if the distribution level forecasting is to be performed centrally where system-level forecasting is being performed. In order to address these challenges, this thesis conducts a systematic study of the scalability and performance of time series forecasting techniques on smart meter data for distribution level short-term energy consumption. The conducted study is based on strategies to parallelize standard and online forecasting algorithms. The developed strategies are converted into algorithms to be implemented for performance evaluation. The performance of these algorithms is evaluated using data collected from several loads during different seasons. Test results demonstrate the challenges of including seasonality terms, and model training when using ARIMA based times series forecasting. Additional results show that the online algorithm achieves better scalability and shorter execution times when compared to the standard ARIMA implementation.

Demand-side Flexibility in Smart Grid

Author : Roya Ahmadiahangar
Publisher : Springer Nature
ISBN 13 : 9811546274
Total Pages : 66 pages
Book Rating : 4.8/5 (115 download)

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Book Synopsis Demand-side Flexibility in Smart Grid by : Roya Ahmadiahangar

Download or read book Demand-side Flexibility in Smart Grid written by Roya Ahmadiahangar and published by Springer Nature. This book was released on 2020-05-08 with total page 66 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights recent advances in the identification, prediction and exploitation of demand side (DS) flexibility and investigates new methods of predicting DS flexibility at various different power system (PS) levels. Renewable energy sources (RES) are characterized by volatile, partially unpredictable and mostly non-dispatchable generation. The main challenge in terms of integrating RES into power systems is their intermittency, which negatively affects the power balance. Addressing this challenge requires an increase in the available PS flexibility, which in turn requires accurate estimation of the available flexibility on the DS and aggregation solutions at the system level. This book discusses these issues and presents solutions for effectively tackling them.

Smart Energy Management

Author : Kaile Zhou
Publisher : Springer Nature
ISBN 13 : 9811693609
Total Pages : 317 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Smart Energy Management by : Kaile Zhou

Download or read book Smart Energy Management written by Kaile Zhou and published by Springer Nature. This book was released on 2022-02-04 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a relatively whole view of data-driven decision-making methods for energy service innovation and energy system optimization. Through personalized energy services provision and energy efficiency improvement, the book can contribute to the green transformation of energy system and the sustainable development of the society. The book gives a new way to achieve smart energy management, based on various data mining and machine learning methods, including fuzzy clustering, shape-based clustering, ensemble clustering, deep learning, and reinforcement learning. The applications of these data-driven methods in improving energy efficiency and supporting energy service innovation are presented. Moreover, this book also investigates the role of blockchain in supporting peer-to-peer (P2P) electricity trading innovation, thus supporting smart energy management. The general scope of this book mainly includes load clustering, load forecasting, price-based demand response, incentive-based demand response, and energy blockchain-based electricity trading. The intended readership of the book includes researchers and engineers in related areas, graduate and undergraduate students in university, and some other general interested audience. The important features of the book are: (1) it introduces various data-driven methods for achieving different smart energy management tasks; (2) it investigates the role of data-driven methods in supporting various energy service innovation; and (3) it explores energy blockchain in P2P electricity trading, and thus supporting smart energy management.

Hybrid Intelligent Approaches for Smart Energy

Author : John A
Publisher : John Wiley & Sons
ISBN 13 : 1119821851
Total Pages : 341 pages
Book Rating : 4.1/5 (198 download)

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Book Synopsis Hybrid Intelligent Approaches for Smart Energy by : John A

Download or read book Hybrid Intelligent Approaches for Smart Energy written by John A and published by John Wiley & Sons. This book was released on 2022-09-30 with total page 341 pages. Available in PDF, EPUB and Kindle. Book excerpt: HYBRID INTELLIGENT APPROACHES FOR SMART ENERGY Green technologies and cleaner energy are two of the most important topics facing our world today, and the march toward efficient energy systems, smart cities, and other green technologies, has been, and continues to be, a long and intricate one. Books like this one keep the veteran engineer and student, alike, up to date on current trends in the technology and offer a reference for the industry for its practical applications. Energy optimization and consumption prediction are necessary to prevent energy waste, schedule energy usage, and reduce the cost. Today, smart computing technologies are slowly replacing the traditional computational methods in energy optimization, consumption, scheduling, and usage. Smart computing is an important core technology in today’s scientific and engineering environment. Smart computation techniques such as artificial intelligence, machine learning, deep learning and Internet of Things (IoT) are the key role players in emerging technologies across different applications, industries, and other areas. These newer, smart computation techniques are incorporated with traditional computation and scheduling methods to reduce power usage in areas such as distributed environment, healthcare, smart cities, agriculture and various functional areas. The scope of this book is to bridge the gap between traditional power consumption methods and modern consumptions methods using smart computation methods. This book addresses the various limitations, issues and challenges of traditional energy consumption methods and provides solutions for various issues using modern smart computation technologies. These smart technologies play a significant role in power consumption, and they are cheaper compared to traditional technologies. The significant limitations of energy usage and optimizations are rectified using smart computations techniques, and the computation techniques are applied across a wide variety of industries and engineering areas. Valuable as reference for engineers, scientists, students, and other professionals across many areas, this is a must-have for any library.

Energy Demand Forecasting

Author : United States. Congress. House. Committee on Science and Technology. Subcommittee on Investigations and Oversight
Publisher :
ISBN 13 :
Total Pages : 386 pages
Book Rating : 4.0/5 (18 download)

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Book Synopsis Energy Demand Forecasting by : United States. Congress. House. Committee on Science and Technology. Subcommittee on Investigations and Oversight

Download or read book Energy Demand Forecasting written by United States. Congress. House. Committee on Science and Technology. Subcommittee on Investigations and Oversight and published by . This book was released on 1981 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Intelligent Optimization Modelling in Energy Forecasting

Author : Wei-Chiang Hong
Publisher :
ISBN 13 : 9783039283651
Total Pages : 262 pages
Book Rating : 4.2/5 (836 download)

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Book Synopsis Intelligent Optimization Modelling in Energy Forecasting by : Wei-Chiang Hong

Download or read book Intelligent Optimization Modelling in Energy Forecasting written by Wei-Chiang Hong and published by . This book was released on 2020 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accurate energy forecasting is important to facilitate the decision-making process in order to achieve higher efficiency and reliability in power system operation and security, economic energy use, contingency scheduling, the planning and maintenance of energy supply systems, and so on. In recent decades, many energy forecasting models have been continuously proposed to improve forecasting accuracy, including traditional statistical models (e.g., ARIMA, SARIMA, ARMAX, multi-variate regression, exponential smoothing models, Kalman filtering, Bayesian estimation models, etc.) and artificial intelligence models (e.g., artificial neural networks (ANNs), knowledge-based expert systems, evolutionary computation models, support vector regression, etc.). Recently, due to the great development of optimization modeling methods (e.g., quadratic programming method, differential empirical mode method, evolutionary algorithms, meta-heuristic algorithms, etc.) and intelligent computing mechanisms (e.g., quantum computing, chaotic mapping, cloud mapping, seasonal mechanism, etc.), many novel hybrid models or models combined with the above-mentioned intelligent-optimization-based models have also been proposed to achieve satisfactory forecasting accuracy levels. It is important to explore the tendency and development of intelligent-optimization-based modeling methodologies and to enrich their practical performances, particularly for marine renewable energy forecasting.