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

Mixed-Variable PSO with Fairness on Multi-Objective Field Data Replication in Wireless Networks

Digital twins have shown a great potential in supporting the development of wireless networks. They are virtual representations of 5G/6G systems enabling the design of machine learning and optimization-based techniques. Field data replication is one of the critical aspects of building a simulation-based twin, where the objective is to calibrate the simulation to match field performance measurements. Since wireless networks involve a variety of key performance indicators (KPIs), the replication process becomes a multi-objective optimization problem in which the purpose is to minimize the error between the simulated and field data KPIs. Unlike previous works, we focus on designing a data-driven search method to calibrate the simulator and achieve accurate and reliable reproduction of field performance. This work proposes a search-based algorithm based on mixedvariable particle swarm optimization (PSO) to find the optimal simulation parameters. Furthermore, we extend this solution to account for potential conflicts between the KPIs using {\alpha}-fairness concept to adjust the importance attributed to each KPI during the search. Experiments on field data showcase the effectiveness of our approach to (i) improve the accuracy of the replication, (ii) enhance the fairness between the different KPIs, and (iii) guarantee faster convergence compared to other methods.Comment: Accepted in International Conference on Communications (ICC) 202

Artificial intelligence in wind speed forecasting: a review

Wind energy production has had accelerated growth in recent years, reaching an annual increase of 17% in 2021. Wind speed plays a crucial role in the stability required for power grid operation. However, wind intermittency makes accurate forecasting a complicated process. Implementing new technologies has allowed the development of hybrid models and techniques, improving wind speed forecasting accuracy. Additionally, statistical and artificial intelligence methods, especially artificial neural networks, have been applied to enhance the results. However, there is a concern about identifying the main factors influencing the forecasting process and providing a basis for estimation with artificial neural network models. This paper reviews and classifies the forecasting models used in recent years according to the input model type, the pre-processing and post-processing technique, the artificial neural network model, the prediction horizon, the steps ahead number, and the evaluation metric. The research results indicate that artificial neural network (ANN)-based models can provide accurate wind forecasting and essential information about the specific location of potential wind use for a power plant by understanding the future wind speed values

Adaptive ML-based technique for renewable energy system power forecasting in hybrid PV-Wind farms power conversion systems

Large scale integration of renewable energy system with classical electrical power generation system requires a precise balance to maintain and optimize the supply–demand limitations in power grids operations. For this purpose, accurate forecasting is needed from wind energy conversion systems (WECS) and solar power plants (SPPs). This daunting task has limits with long-short term and precise term forecasting due to the highly random nature of environmental conditions. This paper offers a hybrid variational decomposition model (HVDM) as a revolutionary composite deep learning-based evolutionary technique for accurate power production forecasting in microgrid farms. The objective is to obtain precise short-term forecasting in five steps of development. An improvised dynamic group-based cooperative search (IDGC) mechanism with a IDGC-Radial Basis Function Neural Network (IDGC-RBFNN) is proposed for enhanced accurate short-term power forecasting. For this purpose, meteorological data with time series is utilized. SCADA data provide the values to the system. The improvisation has been made to the metaheuristic algorithm and an enhanced training mechanism is designed for the short term wind forecasting (STWF) problem. The results are compared with two different Neural Network topologies and three heuristic algorithms: particle swarm intelligence (PSO), IDGC, and dynamic group cooperation optimization (DGCO). The 24 h ahead are studied in the experimental simulations. The analysis is made using seasonal behavior for year-round performance analysis. The prediction accuracy achieved by the proposed hybrid model shows greater results. The comparison is made statistically with existing works and literature showing highly effective accuracy at a lower computational burden. Three seasonal results are compared graphically and statistically.publishedVersio

A Novel Hybrid Spotted Hyena-Swarm Optimization (HS-FFO) Framework for Effective Feature Selection in IOT Based Cloud Security Data

Internet of Things (IoT) has gained its major insight in terms of its deployment and applications. Since IoT exhibits more heterogeneous characteristics in transmitting the real time application data, these data are vulnerable to many security threats. To safeguard the data, machine and deep learning based security systems has been proposed. But this system suffers the computational burden that impedes threat detection capability. Hence the feature selection plays an important role in designing the complexity aware IoT systems to defend the security attacks in the system. This paper propose the novel ensemble of spotted hyena with firefly algorithm to choose the best features and minimise the redundant data features that can boost the detection system's computational effectiveness.  Firstly, an effective firefly optimized feature correlation method is developed.  Then, in order to enhance the exploration and search path, operators of fireflies are combined with Spotted Hyena to assist the swarms in leaving the regionally best solutions. The experimentation has been carried out using the different IoT cloud security datasets such as NSL-KDD-99 , UNSW and CIDCC -001 datasets and contrasted with ten cutting-edge feature extraction techniques, like PSO (particle swarm optimization), BAT, Firefly, ACO(Ant Colony Optimization), Improved PSO, CAT, RAT, Spotted Hyena, SHO and  BOC(Bee-Colony Optimization) algorithms. Results demonstrates the proposed hybrid model has achieved the better feature selection mechanism with less convergence  time and aids better for intelligent threat detection system with the high performance of detection

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Multiple decomposition-aided long short-term memory network for enhanced short-term wind power forecasting.

With the increasing penetration of grid-scale wind energy systems, accurate wind power forecasting is critical to optimizing their integration into the power system, ensuring operational reliability, and enabling efficient system asset utilization. Addressing this challenge, this study proposes a novel forecasting model that combines the long-short-term memory (LSTM) neural network with two signal decomposition techniques. The EMD technique effectively extracts stable, stationary, and regular patterns from the original wind power signal, while the VMD technique tackles the most challenging high-frequency component. A deep learning-based forecasting model, i.e. the LSTM neural network, is used to take advantage of its ability to learn from longer sequences of data and its robustness to noise and outliers. The developed model is evaluated against LSTM models employing various decomposition methods using real wind power data from three distinct offshore wind farms. It is shown that the two-stage decomposition significantly enhances forecasting accuracy, with the proposed model achieving R2 values up to 9.5% higher than those obtained using standard LSTM models

Applications of two neuro-based metaheuristic techniques in evaluating ground vibration resulting from tunnel blasting

Peak particle velocity (PPV) caused by blasting is an unfavorable environmental issue that can damage neighboring structures or equipment. Hence, a reliable prediction and minimization of PPV are essential for a blasting site. To estimate PPV caused by tunnel blasting, this paper proposes two neuro-based metaheuristic models: neuro-imperialism and neuro-swarm. The prediction was made based on extensive observation and data collecting from a tunnelling project that was concerned about the presence of a temple near the blasting operations and tunnel site. A detailed modeling procedure was conducted to estimate PPV values using both empirical methods and intelligence techniques. As a fair comparison, a base model considered a benchmark in intelligent modeling, artificial neural network (ANN), was also built to predict the same output. The developed models were evaluated using several calculated statistical indices, such as variance account for (VAF) and a-20 index. The empirical equation findings revealed that there is still room for improvement by implementing other techniques. This paper demonstrated this improvement by proposing the neuro-swarm, neuro-imperialism, and ANN models. The neuro-swarm model outperforms the others in terms of accuracy. VAF values of 90.318% and 90.606% and a-20 index values of 0.374 and 0.355 for training and testing sets, respectively, were obtained for the neuro-swarm model to predict PPV induced by blasting. The proposed neuro-based metaheuristic models in this investigation can be utilized to predict PPV values with an acceptable level of accuracy within the site conditions and input ranges used in this study

A short-term hybrid wind speed prediction model based on decomposition and improved optimization algorithm

Introduction: In the field of wind power generation, short-term wind speed prediction plays an increasingly important role as the foundation for effective utilization of wind energy. However, accurately predicting wind speed is highly challenging due to its complexity and randomness in practical applications. Currently, single algorithms exhibit poor accuracy in short-term wind speed prediction, leading to the widespread adoption of hybrid wind speed prediction models based on deep learning techniques. To comprehensively enhance the predictive performance of short-term wind speed models, this study proposes a hybrid model, VMDAttention LSTM-ASSA, which consists of three stages: decomposition of the original wind speed sequence, prediction of each mode component, and weight optimization.Methods: To comprehensively enhance the predictive performance of short-term wind speed models, this study proposes a hybrid model, VMDAttention LSTM-ASSA, which consists of three stages: decomposition of the original wind speed sequence, prediction of each mode component, and weight optimization. Firstly, the model incorporates an attention mechanism into the LSTM model to extract important temporal slices from each mode component, effectively improving the slice prediction accuracy. Secondly, two different search operators are introduced to enhance the original Salp Swarm Algorithm, addressing the issue of getting trapped in local optima and achieving globally optimal short-term wind speed predictions.Result: Through comparative experiments using multiple-site short-term wind speed datasets, this study demonstrates that the proposed VMD-AtLSTM-ASSA model outperforms other hybrid prediction models (VMD-RNN, VMD-BPNN, VMD-GRU, VMD-LSTM) with a maximum reduction of 80.33% in MAPE values. The experimental results validate the high accuracy and stability of the VMD-AtLSTM-ASSA model.Discussion: Short-term wind speed prediction is of paramount importance for the effective utilization of wind power generation, and our research provides strong support for enhancing the efficiency and reliability of wind power generation systems. Future research directions may include further improvements in model performance and extension into other meteorological and environmental application domains