Convolutional Neural Network – Based Algorithm for Currency Exchange Rate Prediction

The foreign exchange market is one of the complex monetary markets in the world. Each day trillions of dollars are traded in the FOREX market by banks, retail traders, corporations, and individuals. It is very challenging to predict the price in advance due to the complex, volatile and high fluctuation. Investors and traders are constantly searching for innovative ways to outperform the market and increase their profits. As an outcome, forecasting models are continually being developed by scholars around the globe to accurately predict the characteristics of this nascent market. This study intends to apply the Random Forest (RF) approach to Convolutional Neural Networks, which involves two key steps. The first step is starting with feature selection using Convolutional neural network.The attention layer is then employed to assign weight.The random forest strategy is designed in the second stage to generate high-quality feature subsets. Thus the better result generated by CNN-RF model. Actually, this strategy combines the advantages of two different strategies to produce an outcome that is more consistent with what exchange market decision-makers anticipate happening in the exchange market.The main currency pairs considered in this study's proposed model for predicting exchange rates five and ten minutes in advance are the British Pound Sterling (GBP) against the US Dollar (USD), the Australian Dollar (AUD) against the US Dollar (USD), and the European Euro (EUR) against the Canadian Dollar (CAD) are also used to evaluate the performance of the proposed model.   In compared to the other three models (Multi-Layer Perceptron, Autoregressive Integrated Moving Average, and Recurrent Neural Network), CNN-RF yields better results. This conclusion has been backed by a large body of empirical research, which also suggested that this methodology be regularly used due to its high efficacy. &nbsp

Energy management of micro-grid using cooperative game theory

Micro-grid (MG) has been introduced as a low voltage and a very small power system connected to a distribution grid through the point of common coupling. It consists of distributed energy resources (DERs) such as solar Photovoltaic (PV), wind turbine, fuel cell, etc.), interconnected load and energy storage sources. It can operate in grid-connected (i.e. when connected to the main grid) or islanded (i.e. when not connected to the main grid) mode. It has an advantage of utilizing low carbon sources and the possibility of its use in the remote local environment, which means that the transmission infrastructures and their associated costs may be deferred. Although there has been a proliferation of optimization methods of energy management in the MG, most of these methods consider self-interest of the players in profit distribution. Moreover, only a few of them consider a fair profit distribution using Nash bargaining solution (NBS) (i.e. when utility function is linear) leading to even profit distribution and high degree of dissatisfaction. For the MG to achieve better economic outcomes, a novel method based on weighted fair energy management among the participants (i.e. building of different types, such as residential buildings, schools, and shops) is proposed. The novelty of the proposed method lies in the new profit sharing method to favour certain participant by assigning a weight to each participant with cooperative game theory (CGT) approach using generalized Nash bargaining solution (GNBS). The proposed approach achieves a fair (reasonable or just) profit allocation with negotiating power indicator. In this work, a case study of six different participant sites is proposed using the CGT method of energy management. The proposed method is able to cope with the drawbacks of the existing independent method, which negotiate directly with other participants for selfish profit distribution. It is demonstrated that the independent method results in (1) a reduction in the profit of each participant of MG when compared with CGT approach and (2) the variation of transfer prices in some participants having profit below the specified lower bound profit since the method does not take into consideration the lower profit bounds. The use of CGT method (i.e. when participants form a coalition) to finding multi-partner profit level subject to specified lower bounds is demonstrated. This results in (1) increase in the profit of the MG participants (2) maintaining the profit level of all the participants above status-quo profit (lower specified profit bounds) with variation in transfer prices and (3) allowing certain participant to be favoured by assigning higher negotiating power to such participant. To achieve the optimal solution in the proposed method, a teaching-learning-based optimization (TLBO) algorithm is presented to efficiently solve the problem. For TLBO algorithm, no specific control parameters are needed except the number of generations and population size. This is in contrast with other heuristic algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO) that require other control parameters (i.e. GA requires selection and crossover operation, while PSO makes use of social parameters and cognitive weight). To demonstrate the effectiveness of the proposed TLBO method, the profit allocations are tested in the grid-connected and the islanded mode using both the CGT and the independent method. In this work, the proposed TLBO method is compared with one traditional method, i.e. Lambda iteration method and two heuristic methods, i.e. PSO and GA. Thus, by using TLBO a considerable amount of computation time is saved. Using the same parameter setting for all the heuristic algorithms used, 20 trials are performed to be able to compare the quality of solution and convergence characteristics. The investigation reveals that TLBO gives the highest quality solutions and better convergence characteristics compared to PSO and GA

Stock price predictive analysis : An application of hybrid barnacles mating optimizer with artificial neural network

Artificial Neural Network (ANN) is an effective machine learning technique for addressing regression tasks. Nonetheless, the performance of ANN is highly dependent on the values of its parameters, specifically the weight and bias. To improve its predictive generalization, it is crucial to optimize these parameters. In this study, the Barnacles Mating Optimizer (BMO) is employed as an optimization tool to automatically optimize these parameters. As a relatively new optimization algorithm, it has been shown to be effective in addressing various optimization problems. The proposed hybrid predictive model of BMO-ANN is tested on time series data of stock price using six selected inputs to predict the next day’ closing prices. Evaluated based on Mean Square Error (MSE) and Root Mean Square Error (RMSPE), the proposed BMO-ANN exhibits significant superiority over the other identified hybrid algorithms. Additionally, the difference in means between BMO-ANN and other identified hybrid algorithms was found to be statistically significant, with a significance level of 0.05%

Tea Category Identification Using a Novel Fractional Fourier Entropy and Jaya Algorithm

This work proposes a tea-category identification (TCI) system, which can automatically determine tea category from images captured by a 3 charge-coupled device (CCD) digital camera. Three-hundred tea images were acquired as the dataset. Apart from the 64 traditional color histogram features that were extracted, we also introduced a relatively new feature as fractional Fourier entropy (FRFE) and extracted 25 FRFE features from each tea image. Furthermore, the kernel principal component analysis (KPCA) was harnessed to reduce 64 + 25 = 89 features. The four reduced features were fed into a feedforward neural network (FNN). Its optimal weights were obtained by Jaya algorithm. The 10 × 10-fold stratified cross-validation (SCV) showed that our TCI system obtains an overall average sensitivity rate of 97.9%, which was higher than seven existing approaches. In addition, we used only four features less than or equal to state-of-the-art approaches. Our proposed system is efficient in terms of tea-category identification

Quantum Monte Carlo simulations for estimating FOREX markets: A speculative attacks experience

The foreign exchange markets, renowned as the largest financial markets globally, also stand out as one of the most intricate due to their substantial volatility, nonlinearity, and irregular nature. Owing to these challenging attributes, various research endeavors have been undertaken to effectively forecast future currency prices in foreign exchange with precision. The studies performed have built models utilizing statistical methods, being the Monte Carlo algorithm the most popular. In this study, we propose to apply Auxiliary-Field Quantum Monte Carlo to increase the precision of the FOREX markets models from different sample sizes to test simulations in different stress contexts. Our findings reveal that the implementation of Auxiliary-Field Quantum Monte Carlo significantly enhances the accuracy of these models, as evidenced by the minimal error and consistent estimations achieved in the FOREX market. This research holds valuable implications for both the general public and financial institutions, empowering them to effectively anticipate significant volatility in exchange rate trends and the associated risks. These insights provide crucial guidance for future decision-making processes

Active congestion quantification and reliability improvement considering aging failure in modern distribution networks

The enormous concerns of climate change and traditional resource crises lead to the increased use of distributed generations (DGs) and electric vehicles (EVs) in distribution networks. This leads to significant challenges in maintaining safe and reliable network operations due to the complexity and uncertainties in active distribution networks, e.g., congestion and reliability problems. Effective congestion management (CM) policies require appropriate indices to quantify the seriousness and customer contributions to congested areas. Developing an accurate model to identify the residual life of aged equipment is also essential in long-term CM procedures. The assessment of network reliability and equipment end-of-life failure also plays a critical role in network planning and regulation. The main contributions of this thesis include a) outlining the specific characteristics of congestion events and introducing the typical metrics to assess the effectiveness of CM approaches; b) proposing spatial, temporal and aggregate indices for rapidly recognizing the seriousness of congestion in terms of thermal and voltage violations, and proposing indices for quantifying the customer contributions to congested areas; c) proposing an improved method to estimate the end-of-life failure probabilities of transformers and cables lines taking real-time relative aging speed and loss-of-life into consideration; d) quantifying the impact of different levels of EV penetration on the network reliability considering end-of-life failure on equipment and post-fault network reconfiguration; and e) proposing an EV smart charging optimization model to improve network reliability and reduce the cost of customers and power utilities. Simulation results illustrate the feasibility of the proposed indices in rapidly recognizing the congestion level, geographic location, and customer contributions in balanced and unbalanced systems. Voltage congestion can be significantly relieved by network reconfiguration and the utilization of the proposed indices by utility operators in CM procedures is also explained. The numerical studies also verify that the improved Arrhenius-Weibull can better indicate the aging process and demonstrate the superior accuracy of the proposed method in identifying residual lives and end-of-life failure probabilities of transformers and conductors. The integration of EV has a great impact on equipment aging failure probability and loss-of-life, thus resulting in lower network reliability and higher cost for managing aging failure. Finally, the proposed piecewise linear optimization model of the EV smart charging framework can significantly improve network reliability by 90% and reduce the total cost by 83.8% for customers and power utilities

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

Short-Term Solar Power Forecasting Based on CEEMDAN and Kernel Extreme Learning Machine

The use of renewable energy sources contributes to environmental awareness and sustainable development policy. The inexhaustible and nonpolluting nature of solar energy has attracted worldwide attention. Accurate forecasting of solar power is vital for the reliability and stability of power systems. However, the effect of the intermittency nature of solar radiation makes the development of accurate prediction models challenging. This paper presents a hybrid model based on Kernel Extreme Learning Machine (Kernel-ELM) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for short-term solar power forecasting. The decomposition technique increases the number of stable, stationary, and regular patterns of the original signals. Each decomposed signal is fed into Kernel-ELM. To validate the performance of the hybrid model, solar power data from the BSEU Renewable Energy Laboratory, measured at 5-minute intervals, are used. To validate the proposed model, its performance is compared to some state-of-the-art forecasting models with seasonal data. The results highlight the good performance of the proposed hybrid model compared to other classical algorithms according to the metrics

Symbiotic Organisms Search Algorithm: theory, recent advances and applications

The symbiotic organisms search algorithm is a very promising recent metaheuristic algorithm. It has received a plethora of attention from all areas of numerical optimization research, as well as engineering design practices. it has since undergone several modifications, either in the form of hybridization or as some other improved variants of the original algorithm. However, despite all the remarkable achievements and rapidly expanding body of literature regarding the symbiotic organisms search algorithm within its short appearance in the field of swarm intelligence optimization techniques, there has been no collective and comprehensive study on the success of the various implementations of this algorithm. As a way forward, this paper provides an overview of the research conducted on symbiotic organisms search algorithms from inception to the time of writing, in the form of details of various application scenarios with variants and hybrid implementations, and suggestions for future research directions