Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers. This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

The Weighted Support Vector Machine Based on Hybrid Swarm Intelligence Optimization for Icing Prediction of Transmission Line

Not only can the icing coat on transmission line cause the electrical fault of gap discharge and icing flashover but also it will lead to the mechanical failure of tower, conductor, insulators, and others. It will bring great harm to the people’s daily life and work. Thus, accurate prediction of ice thickness has important significance for power department to control the ice disaster effectively. Based on the analysis of standard support vector machine, this paper presents a weighted support vector machine regression model based on the similarity (WSVR). According to the different importance of samples, this paper introduces the weighted support vector machine and optimizes its parameters by hybrid swarm intelligence optimization algorithm with the particle swarm and ant colony (PSO-ACO), which improves the generalization ability of the model. In the case study, the actual data of ice thickness and climate in a certain area of Hunan province have been used to predict the icing thickness of the area, which verifies the validity and applicability of this proposed method. The predicted results show that the intelligent model proposed in this paper has higher precision and stronger generalization ability

메타 휴리스틱 최적화를 이용한 기계학습 기반 선박 건조 공정 리드 타임 예측

학위논문(석사) -- 서울대학교대학원 : 공과대학 조선해양공학과, 2021.8. 하오유주.In the shipbuilding industry, each production process has a respective lead time; that is, the duration between start and finish times. Lead time is basic data that is necessary for high-efficiency production planning and systematic production management. Therefore, lead time must be accurate. However, the traditional method of lead time management is not scientific because it mostly makes the plan by calculating the average lead times derived from historical data. Therefore, to understand the complex relationship between lead time and other influencing factors, this study proposes to use machine learning (ML) algorithms, support vector machine (SVM) and artificial neural network (ANN), which are frequently applied in prediction fields. Moreover, to improve prediction accuracy, this study proposes to apply meta-heuristic algorithms to optimize the parameters of the ML models. This thesis builds hybrid models, including meta-heuristic-ANN, meta-heuristic-SVM models. In addition, this study compares model’s performance with each other. In searching for the ML model’s parameters, the results point out that the new self-organizing hierarchical particle swarm optimization (PSO) with jumping time-varying acceleration coefficients (NHPSO-JTVAC) algorithm is superior in terms of performance. More importantly, the test results demonstrate that the integrated models, based on NHPSO-JTVAC, have the smallest mean absolute percentage error (MAPE) test error in the three shipyard block process data sets, 11.79%, 16.03% and 16.45%, respectively. The results also demonstrate that the built models based on NHPSO-JTVAC can achieve further meaningful enhancements in terms of prediction accuracy. Overall, the NHPSO–JTVAC-SVM, NHPSO–JTVAC-ANN models are feasible for predicting the lead time in shipbuilding.조선 산업에서 각 공정은 리드 타임을 가진다. 리드 타임이란 공정 시작과 종료 간에 시간으로, 고효율의 생산계획과 체계적 생산관리를 위해 매우 중요한 지표이다. 특히, 생산 계획 단계에서 정확한 리드타임 예측은 납기 준수를 위한 계획 수립을 위해 매우 중요하다. 그러나 기존의 예측 방법은 과거 데이터의 평균값을 사용했기 때문에 정확도가 매우 떨어졌다. 따라서 본 연구에서는 리드 타임과 다른 영향 요인 간의 복잡한 관계를 이해하기 위해 예측 분야에서 자주 적용되는 머신 러닝 (ML) 모델인 서포트 벡터 머신 (SVM) 및 인공 신경망 (ANN) 적용을 제안한다. 또한, 기계학습 모델 예측 정확도를 향상시키기 위해 메타 휴리스틱 알고리즘을 적용하여 모델의 파라미터를 최적화하고자 한다. 본 연구는 meta-heuristics-ANN, meta-heuristics-SVM 모델을 포함하는 하이브리드 모델을 구축한다. 더불어, 본 연구는 메타 휴리스틱 알고리즘 기반으로 최적화된 기계학습 모델의 성능을 서로 비교한다. 연구 결과를 통해, ML 모델의 파라미터를 탐색하는 과정에서 particle swam optimization (PSO)의 enhanced 버전인 NHPSO-JTVAC 알고리즘이 탐색 성능 면에서 다른 알고리즘보다 우수하다는 것을 알 수 있다. 뿐만 아니라 테스트 결과를 살펴보면 NHPSO-JTVAC에 기반한 하이브리드 모델이 조선소 세 개의 블록 공정 데이터에서 (각각 11.79%, 16.03% 및 16.45%) 가장 작은 MAPE 테스트 오차임을 알 수 있다. 이것은 NHPSO-JTVAC를 기반으로 구축된 모델이 예측 정확도 측면에서 의미 있는 향상을 더 달성할 수 있음을 보여준다. 전반적으로 NHPSO-JTVAC-SVM, NHPSO-JTVAC-ANN 모델은 조선소 블록 공정의 리드 타임을 예측하는 데 적합하다는 것을 확인할 수 있다.Chapter 1 Introduction 1 1.1 Background and Motivation 1 1.2 Related Works 3 1.2.1 Related Works for Lead Time Prediction 3 1.2.2 Related Works for Hybrid Predictive Model 4 1.3 Thesis Organization 6 Chapter 2 Machine Learning 7 2.1 Support Vector Machine 7 2.1.1 Support Vector Machine Algorithm 7 2.1.2 Hyperparameter Optimization for SVM 10 2.2 Artificial Neural Network 11 2.2.1 Artificial Neural Network Algorithm 11 2.2.2 Hyperparameter Optimization for ANN 15 Chapter 3 Meta-heuristic Optimization Algorithms 17 3.1 Particle Swarm Optimization 17 3.2 NHPSO-JTVAC: An Advanced Version of PSO 18 3.3 Bat Algorithm 19 3.4 Firefly Algorithm 21 3.5 Grasshopper Optimization Algorithm 22 3.6 Moth Search Algorithm 24 Chapter 4 Hybrid Artificial Intelligence Models 27 4.1 Hybrid Meta-heuristic-SVM Models 27 4.1.1 Hybrid PSO-SVM Model 29 4.1.2 Hybrid NHPSO-JTVAC-SVM Model 30 4.1.3 Hybrid BA-SVM Model 31 4.1.4 Hybrid FA-SVM Model 33 4.1.5 Hybrid GOA-SVM Model 34 4.1.6 Hybrid MSA-SVM Model 35 4.2 Hybrid Meta-heuristic-ANN Models 36 4.2.1 Hybrid PSO-ANN Model 38 4.2.2 Hybrid NHPSO-JTVAC-ANN Model 39 4.2.3 Hybrid BA-ANN Model 40 4.2.4 Hybrid FA-ANN Model 41 4.2.5 Hybrid GOA-ANN Model 42 4.2.6 Hybrid MSA-ANN Model 43 Chapter 5 Lead Time Prediction Based on Hybrid AI Models 44 5.1 Data and Preparation 44 5.1.1 Data Normalization 45 5.1.2 Feature Selection 45 5.2 Lead Time Prediction 46 5.3 Performance Metrics 47 Chapter 6 Experimental Results 49 6.1 Results Based on Hybrid SVM-based Models 49 6.2 Results Based on Hybrid ANN-based Models 55 6.3 Overall Results 60 Chapter 7 Conclusions and Future Works 62 Bibliography 63 Appendix A 68 Abstract in Korean 69석

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Modeling and Optimal Operation of Hydraulic, Wind and Photovoltaic Power Generation Systems

The transition to 100% renewable energy in the future is one of the most important ways of achieving "carbon peaking and carbon neutrality" and of reducing the adverse effects of climate change. In this process, the safe, stable and economical operation of renewable energy generation systems, represented by hydro-, wind and solar power, is particularly important, and has naturally become a key concern for researchers and engineers. Therefore, this book focuses on the fundamental and applied research on the modeling, control, monitoring and diagnosis of renewable energy generation systems, especially hydropower energy systems, and aims to provide some theoretical reference for researchers, power generation departments or government agencies

Application of Power Electronics Converters in Smart Grids and Renewable Energy Systems

This book focuses on the applications of Power Electronics Converters in smart grids and renewable energy systems. The topics covered include methods to CO2 emission control, schemes for electric vehicle charging, reliable renewable energy forecasting methods, and various power electronics converters. The converters include the quasi neutral point clamped inverter, MPPT algorithms, the bidirectional DC-DC converter, and the push–pull converter with a fuzzy logic controller

Evolutionary multivariate time series prediction

Multivariate time series (MTS) prediction plays a significant role in many practical data mining applications, such as finance, energy supply, and medical care domains. Over the years, various prediction models have been developed to obtain robust and accurate prediction. However, this is not an easy task by considering a variety of key challenges. First, not all channels (each channel represents one time series) are informative (channel selection). Considering the complexity of each selected time series, it is difficult to predefine a time window used for inputs. Second, since the selected time series may come from cross domains collected with different devices, they may require different feature extraction techniques by considering suitable parameters to extract meaningful features (feature extraction), which influences the selection and configuration of the predictor, i.e., prediction (configuration). The challenge arising from channel selection, feature extraction, and prediction (configuration) is to perform them jointly to improve prediction performance. Third, we resort to ensemble learning to solve the MTS prediction problem composed of the previously mentioned operations,  where the challenge is to obtain a set of models satisfied both accurate and diversity. Each of these challenges leads to an NP-hard combinatorial optimization problem, which is impossible to be solved using the traditional methods since it is non-differentiable. Evolutionary algorithm (EA), as an efficient metaheuristic stochastic search technique, which is highly competent to solve complex combinatorial optimization problems having mixed types of decision variables, may provide an effective way to address the challenges arising from MTS prediction. The main contributions are supported by the following investigations. First, we propose a discrete evolutionary model, which mainly focuses on seeking the influential subset of channels of MTS and the optimal time windows for each of the selected channels for the MTS prediction task. A comprehensively experimental study on a real-world electricity consumption data with auxiliary environmental factors demonstrates the efficiency and effectiveness of the proposed method in searching for the informative time series and respective time windows and parameters in a predictor in comparison to the result obtained through enumeration. Subsequently, we define the basic MTS prediction pipeline containing channel selection, feature extraction, and prediction (configuration). To perform these key operations, we propose an evolutionary model construction (EMC) framework to seek the optimal subset of channels of MTS, suitable feature extraction methods and respective time windows applied to the selected channels, and parameter settings in the predictor simultaneously for the best prediction performance. To implement EMC, a two-step EA is proposed, where the first step EA mainly focuses on channel selection while in the second step, a specially designed EA works on feature extraction and prediction (configuration). A real-world electricity data with exogenous environmental information is used and the whole dataset is split into another two datasets according to holiday and nonholiday events. The performance of EMC is demonstrated on all three datasets in comparison to hybrid models and some existing methods. Then, based on the prediction pipeline defined previously, we propose an evolutionary multi-objective ensemble learning model (EMOEL) by employing multi-objective evolutionary algorithm (MOEA) subjected to two conflicting objectives, i.e., accuracy and model diversity. MOEA leads to a pareto front (PF) composed of non-dominated optimal solutions, where each of them represents the optimal subset of the selected channels, the selected feature extraction methods and the selected time windows, and the selected parameters in the predictor. To boost ultimate prediction accuracy, the models with respect to these optimal solutions are linearly combined with combination coefficients being optimized via a single-objective task-oriented EA. The superiority of EMOEL is identified on electricity consumption data with climate information in comparison to several state-of-the-art models. We also propose a multi-resolution selective ensemble learning model, where multiple resolutions are constructed from the minimal granularity using statistics. At the current time stamp, the preceding time series data is sampled at different time intervals (i.e., resolutions) to constitute the time windows. For each resolution, multiple base learners with different parameters are first trained. Feature selection technique is applied to search for the optimal set of trained base learners and least square regression is used to combine them. The performance of the proposed ensemble model is verified on the electricity consumption data for the next-step and next-day prediction. Finally, based on EMOEL and multi-resolution, instead of only combining the models generated from each PF, we propose an evolutionary ensemble learning (EEL) framework, where multiple PFs are aggregated to produce a composite PF (CPF) after removing the same solutions in PFs and being sorted into different levels of non-dominated fronts (NDFs). Feature selection techniques are applied to exploit the optimal subset of models in level-accumulated NDF and least square is used to combine the selected models. The performance of EEL that chooses three different predictors as base learners is evaluated by the comprehensive analysis of the parameter sensitivity. The superiority of EEL is demonstrated in comparison to the best result from single-objective EA and the best individual from the PF, and several state-of-the-art models across electricity consumption and air quality datasets, both of which use the environmental factors from other domains as the auxiliary factors. In summary, this thesis provides studies on how to build efficient and effective models for MTS prediction. The built frameworks investigate the influential factors, consider the pipeline composed of channel selection, feature extraction, and prediction (configuration) simultaneously, and keep good generalization and accuracy across different applications. The proposed algorithms to implement the frameworks use techniques from evolutionary computation (single-objective EA and MOEA), machine learning and data mining areas. We believe that this research provides a significant step towards constructing robust and accurate models for solving MTS prediction problems. In addition, with the case study on electricity consumption prediction, it will contribute to helping decision-makers in determining the trend of future energy consumption for scheduling and planning of the operations of the energy supply system

Intelligent Condition Monitoring and Prognostic Methods with Applications to Dynamic Seals in the Oil & Gas Industry

The capital-intensive oil & gas industry invests billions of dollars in equipment annually and it is important to keep the equipment in top operating condition to help maintain efficient process operations and improve the rate of return by predicting failures before incidents. Digitalization has taken over the world with advances in sensor technology, wireless communication and computational capabilities, however oil & gas industry has not taken full advantage of this despite being technology centric. Dynamic seals are a vital part of reciprocating and rotary equipment such as compressor, pumps, engines, etc. and are considered most frequently failing component. Polymeric seals are increasingly complex and non-linear in behavior and have been the research of interest since 1950s. Most of the prognostic studies on seals are physics-based and requires direct estimation of different physical parameters to assess the degradation of seals, which are often difficult to obtain during operation. Another feasible approach to predict the failure is from performance related sensor data and is termed as data-driven prognostics. The offline phase of this approach is where the performance related data from the component of interest are acquired, pre-processed and artificial intelligence tools or statistical methods are used to model the degradation of a system. The developed models are then deployed online for a real-time condition monitoring. There is a lack of research on the data-driven based tools and methods for dynamic seal prognosis. The primary goal in this dissertation is to develop offline data-driven intelligent condition monitoring and prognostic methods for two types of dynamic seals used in the oil & gas industry, to avoid fatal breakdown of rotary and reciprocating equipment. Accordingly, the interest in this dissertation lies in developing models to effectively evaluate and classify the running condition of rotary seals; assess the progression of degradation from its incipient to failure and to estimate the remaining useful life (RUL) of reciprocating seals. First, a data-driven prognostic framework is developed to classify the running condition of rotary seals. An accelerated aging and testing procedure simulating rotary seal operation in oil field is developed to capture the behavior of seals through their cycle of operation until failure. The diagnostic capability of torque, leakage and vibration signal in differentiating the health states of rotary seals using experiments are compared. Since the key features that differentiate the health condition of rotary seals are unknown, an extensive feature extraction in time and frequency domain is carried out and a wrapper-based feature selection approach is used to select relevant features, with Multilayer Perceptron neural network utilized as classification technique. The proposed approach has shown that features extracted from torque and leakage lack a better discriminating power on its own, in classifying the running condition of seals throughout its service life. The classifier built using optimal set of features from torque and leakage collectively has resulted in a high classification accuracy when compared to random forest and logistic regression, even for the data collected at a different operating condition. Second, a data-driven approach to predict the degradation process of reciprocating seals based on friction force signal using a hybrid Particle Swarm Optimization - Support Vector Machine is presented. There is little to no knowledge on the feature that reflects the degradation of reciprocating seals and on the application of SVM in predicting the future running condition of polymeric components such as seals. Controlled run-to-failure experiments are designed and performed, and data collected from a dedicated experimental set-up is used to develop the proposed approach. A degradation feature with high monotonicity is used as an indicator of seal degradation. The pseudo nearest neighbor is used to determine the essential number of inputs for forecasting the future trend. The most challenging aspect of tuning parameters in SVM is framed in terms of an optimization problem aimed at minimizing the prediction error. The results indicate the effectiveness and better accuracy of the proposed approach when compared to GA-SVM and XGBoost. Finally, a deep neural network-based approach for estimating remaining useful life of reciprocating seals, using force and leakage signals is presented. Time domain and frequency domain statistical features are extracted from the measurements. An ideal prognostic feature should be well correlated with degradation time, monotonically increasing or decreasing and robust to outliers. The identified metrics namely: monotonicity, correlation and robustness are used to evaluate the goodness of extracted features. Each of the three metric carries a relative importance in the RUL estimation and a weighted linear combination of the metrics are used to rank and select the best set of prognostic features. The redundancy in the selected features is eliminated using Kelley-Gardner-Sutcliffe penalty function-based correlation-clustering algorithm to select a representative feature from each of the clusters. Finally, RUL estimation is modeled using a deep neural network model. Run-to-failure data collected from a reciprocating set-up was used to validate this approach and the findings show that the proposed approach can improve the accuracy of RUL prediction when compared to PSO-SVM and XGBoost regression. This research has important contribution and implications to rotary and reciprocating seal domain in utilizing sensors along with machine learning algorithms in assessing the health state and prognosis of seals without any direct measurements. This research has paved the way to move from a traditional fail-and-fix to predict-and-prevent approach in maintenance of seals. The findings of this research are foundational for developing an online degradation assessment platform which can remotely monitor the performance degradation of seals and provide action recommendations on maintenance decisions. This would be of great interest to customers and oil field operators to improve equipment utilization, control maintenance cost by enabling just-in-time maintenance and increase rate of return on equipment by predicting failures before incidents

Data-Intensive Computing in Smart Microgrids

Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data). The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generation and operation in real time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. An efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids, in order to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management. This book contains an assortment of relevant novel research contributions that provide real-world applications of data-intensive analytics in smart grids and contribute to the dissemination of new ideas in this area