Microgrids (MGs) have evolved as critical components of modern energy distribution networks, providing increased dependability, efficiency, and sustainability. Effective control strategies are essential for optimizing MG operation and maintaining stability in the face of changing environmental and load conditions. Traditional rule-based control systems are extensively used due to their interpretability and simplicity. However, these strategies frequently lack the flexibility for complex and changing system dynamics. This paper provides a novel method called hybrid intelligent control for adaptive MG that integrates basic rule-based control and deep learning techniques, including gated recurrent units (GRUs), basic recurrent neural networks (RNNs), and long short-term memory (LSTM). The main target of this hybrid approach is to improve MG management performance by combining the strengths of basic rule-based systems and deep learning techniques. These deep learning techniques readily enhance and adapt control decisions based on historical data and domain-specific rules, leading to increasing system efficiency, stability, and resilience in adaptive MG. Our results show that the proposed method optimizes MG operation, especially under demanding conditions such as variable renewable energy supply and unanticipated load fluctuations. This study investigates special RNN architectures and hyperparameter optimization techniques with the aim of predicting power consumption and generation within the adaptive MG system. Our promising results show the highest-performing models indicating high accuracy and efficiency in power prediction. The finest-performing model accomplishes an $R^2$ value close to 1, representing a strong correlation between predicted and actual power values. Specifically, the best model achieved an $R^2$ value of 0.999809, an MSE of 0.000002, and an MAE of 0.000831.

Microgrids (MGs) have evolved as critical components of modern energy distribution networks, providing increased dependability, efficiency, and sustainability. Effective control strategies are essential for optimising MG operation and maintaining stability in the face of changing environmental and load conditions. Traditional rule-based control systems are extensively used due to their interpretability and simplicity. However, these strategies frequently lack the flexibility for complex and changing system dynamics. This paper provides a novel method called hybrid intelligent control for adaptive MG that integrates basic rule-based-control and deep learning techniques, including gated recurrent units (GRU), basic recurrent neural network (RNN) and long short-term memory (LSTM). The main target of this hybrid approach is to improve MG management performance by combining the strengths of basic rule-based systems and deep learning techniques. These deep learning techniques readily enhance and adapt control decisions based on historical data and domain-specific rules, leading to increasing system efficiency, stability, and resilience in adaptive MG. Our results show that the proposed method optimises MG operation, especially under demanding conditions such as variable renewable energy supply and unanticipated load fluctuations. This study investigates special RNN architectures and hyper-parameter optimization techniques with the aim of predicting power consumption and generation within the adaptive MG system. Our promising results show the highest-performing models indicating high accuracy and efficiency in power prediction. The finest-performing model accomplishes an R2 value close to 1, representing a strong correlation between predicted and actual power values.

Traditional indoor human activity recognition (HAR) has been defined as a time-series data classification problem and requires feature extraction. The current indoor HAR systems still lack transparent, interpretable, and explainable approaches that can generate human-understandable information. This paper proposes a new approach, called Human Activity Recognition on Signal Images (HARSI), which defines the HAR problem as an image classification problem to improve both explainability and recognition accuracy. The proposed HARSI method collects sensor data from the Internet of Things (IoT) environment and transforms the raw signal data into some visual understandable images to take advantage of the strengths of convolutional neural networks (CNNs) in handling image data. This study focuses on the recognition of symmetric human activities, including walking, jogging, moving downstairs, moving upstairs, standing, and sitting. The experimental results carried out on a real-world dataset showed that a significant improvement (13.72%) was achieved by the proposed HARSI model compared to the traditional machine learning models. The results also showed that our method (98%) outperformed the state-of-the-art methods (90.94%) in terms of classification accuracy.

Bu çalışmada, sezgisel ve sayısal yöntemlerin yapısında var olan eksiklikleri gidermek amacıyla melez bir optimizasyon yönteminin geliştirilmesi üzerinde durulmuştur. Sezgisel yöntemler kesin çözümü garanti edemezler. Ancak, sayısal yöntemlerden göreceli olarak daha hızlı çalıştırılabilirler. Öte yandan, sayısal yöntemler ise güçlü matematiksel çözümler içerdikleri için kesin çözüme ulaşabilmektedirler. Uygulamalarımızda, sezgisel optimizasyon yöntemlerinden Parçacık Sürü Optimizasyonu (PSO) yöntemi ile sayısal optimizasyon yöntemlerinden Broydon-Fletcher-Goldfarb-Shanno (BFGS) yöntemi birleştirilerek optimum çözüme daha kesin ve daha hızlı bir şekilde ulaşılması amaçlanmıştır. Geliştirilen optimizasyon yönteminin algoritmasında, çözüm öncelikle BFGS ile aranmaktadır. Böylece, amaç fonksiyonu için en küçük veya en büyük noktalar belirlenmektedir. Daha sonra, PSO ile bu noktalar arasında eleme işlemi gerçekleştirilmektedir. Nihai sonuca ulaşılana kadar, ara çözüm noktaları BFGS ile PSO arasında sürekli aktarılmaktadır. Özgün BFGS ve özgün PSO kullanılarak, önerilen önce BFGS sonra PSO melez yöntemi ve tersi melez yöntem (önce PSO sonra BFGS) olmak üzere iki farklı yöntem; araştırmacılar arasında sıklıkla kullanılan test fonksiyonları üzerinde çalıştırılarak uygulanmıştır.