Enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction

Over the past decades, the Least Squares Support Vector Machines (LSSVM) has been widely utilized in prediction task of various application domains. Nevertheless, existing literature showed that the capability of LSSVM is highly dependent on the value of its hyper-parameters, namely regularization parameter and kernel parameter, where this would greatly affect the generalization of LSSVM in prediction task. This study proposed a hybrid algorithm, based on Artificial Bee Colony (ABC) and LSSVM, that consists of three algorithms; ABC-LSSVM, lvABC-LSSVM and cmABC-LSSVM. The lvABC algorithm is introduced to overcome the local optima problem by enriching the searching behaviour using Levy mutation. On the other hand, the cmABC algorithm that incorporates conventional mutation addresses the over- fitting or under-fitting problem. The combination of lvABC and cmABC algorithm, which is later introduced as Enhanced Artificial Bee Colony–Least Squares Support Vector Machine (eABC-LSSVM), is realized in prediction of non renewable natural resources commodity price. Upon the completion of data collection and data pre processing, the eABC-LSSVM algorithm is designed and developed. The predictability of eABC-LSSVM is measured based on five statistical metrics which include Mean Absolute Percentage Error (MAPE), prediction accuracy, symmetric MAPE (sMAPE), Root Mean Square Percentage Error (RMSPE) and Theils’ U. Results showed that the eABC-LSSVM possess lower prediction error rate as compared to eight hybridization models of LSSVM and Evolutionary Computation (EC) algorithms. In addition, the proposed algorithm is compared to single prediction techniques, namely, Support Vector Machines (SVM) and Back Propagation Neural Network (BPNN). In general, the eABC-LSSVM produced more than 90% prediction accuracy. This indicates that the proposed eABC-LSSVM is capable of solving optimization problem, specifically in the prediction task. The eABC-LSSVM is hoped to be useful to investors and commodities traders in planning their investment and projecting their profit

Gasoline price forecasting: An application of LSSVM with improved ABC

Optimizing the hyper-parameters of Least Squares Support Vector Machines (LSSVM) is crucial as it will directly influence the predictive power of the algorithm.To tackle such issue, this study proposes an improved Artificial Bee Colony (IABC) algorithm which is based on conventional mutation.The IABC serves as an optimizer for LSSVM.Realized in gasoline price forecasting, the performance is guided based on Mean Absolute Percentage Error (MAPE) and Root Mean Square Percentage Error (RMSPE).The conducted simulation results show that, the proposed IABCLSSVM outperforms the results produced by ABC-LSSVM and also the Back Propagation Neural Network

A Brief Analysis of Gravitational Search Algorithm (GSA) Publication from 2009 to May 2013

Gravitational Search Algorithm was introduced in year 2009. Since its introduction, the academic community shows a great interest on this algorith. This can be seen by the high number of publications with a short span of time. This paper analyses the publication trend of Gravitational Search Algorithm since its introduction until May 2013. The objective of this paper is to give exposure to reader the publication trend in the area of Gravitational Search Algorithm

Algorithms for Fault Detection and Diagnosis

Due to the increasing demand for security and reliability in manufacturing and mechatronic systems, early detection and diagnosis of faults are key points to reduce economic losses caused by unscheduled maintenance and downtimes, to increase safety, to prevent the endangerment of human beings involved in the process operations and to improve reliability and availability of autonomous systems. The development of algorithms for health monitoring and fault and anomaly detection, capable of the early detection, isolation, or even prediction of technical component malfunctioning, is becoming more and more crucial in this context. This Special Issue is devoted to new research efforts and results concerning recent advances and challenges in the application of “Algorithms for Fault Detection and Diagnosis”, articulated over a wide range of sectors. The aim is to provide a collection of some of the current state-of-the-art algorithms within this context, together with new advanced theoretical solutions

A Survey on Intelligent Optimization Approaches to Boiler Combustion Optimization

This paper reviews the researches on boiler combustion optimization, which is an important direction in the field of energy saving and emission reduction. Many methods have been used to deal with boiler combustion optimization, among which evolutionary computing (EC) techniques have recently gained much attention. However, the existing researches are not sufficiently focused and have not been summarized systematically. This has led to slow progress of research on boiler combustion optimization and has obstacles in the application. This paper introduces a comprehensive survey of the works of intelligent optimization algorithms in boiler combustion optimization and summarizes the contributions of different optimization algorithms. Finally, this paper discusses new research challenges and outlines future research directions, which can guide boiler combustion optimization to improve energy efficiency and reduce pollutant emission concentrations

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

Applications of Computational Intelligence to Power Systems

In power system operation and control, the basic goal is to provide users with quality electricity power in an economically rational degree for power systems, and to ensure their stability and reliability. However, the increased interconnection and loading of the power system along with deregulation and environmental concerns has brought new challenges for electric power system operation, control, and automation. In the liberalised electricity market, the operation and control of a power system has become a complex process because of the complexity in modelling and uncertainties. Computational intelligence (CI) is a family of modern tools for solving complex problems that are difficult to solve using conventional techniques, as these methods are based on several requirements that may not be true all of the time. Developing solutions with these “learning-based” tools offers the following two major advantages: the development time is much shorter than when using more traditional approaches, and the systems are very robust, being relatively insensitive to noisy and/or missing data/information, known as uncertainty

Integrated Optimization of Location, Design, and Operation of Renewable Energy Systems for Urban Microgrids

The building sector of urban areas plays a crucial role in carbon emissions and climate change. Distributed generation using clean energies could help to reduce emissions. Furthermore, urban microgrids increase the reliability of power supply since most of the power outages are created in the grid distribution system and transmission lines. However, a cost-effective design and operation of an urban microgrid poses challenges, such as limited space for installing the renewable components, especially in populated areas, the uncertainty of renewable resources, and the resiliency of the designed microgrid in case of not having access to the central grid. Therefore, this thesis was initiated with the objective of developing a comprehensive method for the efficient design of an urban microgrid. The developed framework consists of three main modules. The first module aims at designing an energy system for a community microgrid by sizing and finding the optimum configuration of the energy system. To resolve the spatial issue problem in urban areas, regional renewable generation is proposed in this research where clean energy is produced outside of the populated area as a virtual power plant in relation to the microgrid. A mapping model is also developed to select the best location for installing components outside the microgrid. The mapping model is connected with the optimization model to automatically generate the best configuration and location of regional generation based on several aspects of each zone. The second module deals with renewable resources and electrical load demand uncertainties and tries to reduce them by forecasting strategies. Since renewable resources such as solar irradiance and wind speed are not predictable using just historical data, hybridized numeric weather prediction (NWP) and deep learning models are offered to tackle the drawback. The last module proposes a solution to ensure resilience against power supply failures in electricity grids caused by extreme weather conditions, unavailability of generation capacities, and transmission components problems. The discussed models were applied to one of Concordia University's largest buildings in downtown Montreal, Canada. The results show a significant improvement in the environmental aspect of the regional generation if the existing gas boiler would be substituted by electric boilers and heat pumps (using generated renewable electricity outside of microgrid), preventing emissions of about 4233 tons CO2 and 5.3 tons NOX per year. Using a proposed tariff structure beneficial to both the customer and utility, the resulting levelized cost of energy is about 5.3 Cents per kilowatt hour, i.e., lower than the current rate of about 6 Cents per kWh. Using the second module’s proposed hybrid models for renewable resources and electrical load demand prediction of the case study was also helpful by considerably bringing down the error. Finally, operation dispatch scenarios are developed to reinforce the system’s resiliency in severe conditions for the case study in the third module. A mixed-integer linear programming (MILP) approach is employed to identify global optimum dispatch solutions based on the next 48 h plans for different seasons to formulate a whole-year operational model. The results show that the loss of power supply probability (LPSP), as an indicator of resiliency, could be lowered to near zero while minimizing operational cost using the proposed optimal load (derived from critical load)