As short-term load forecasting is essential for the day-to-day operation planning of power systems, we built an ensemble learning model to perform such forecasting for Thai data. The proposed model uses voting regression (VR), producing forecasts with weighted averages of forecasts from five individual models: three parametric multiple linear regressors and two non-parametric machine-learning models. The regressors are linear regression models with gradient-descent (LR), ordinary least-squares (OLS) estimators, and generalized least-squares auto-regression (GLSAR) models. In contrast, the machine-learning models are decision trees (DT) and random forests (RF). To select the best model variables and hyper-parameters, we used cross-validation (CV) performance instead of the test data performance, which yielded overly good test performance. We compared various validation schemes and found that the Blocked-CV scheme gives the validation error closest to the test error. Using Blocked-CV, the test results show that the VR model outperforms all its individual predictors.

To increase the penetration of renewable energy sources, a proper energy storage is required. In this context, there is a growing concern about the use of energy storage for overcoming the intermittency of renewable energy sources as well as in demand side management. In order to accomplish grid synchronization of energy storage, proper power conditioning devices are required. This paper analyses single phase grid synchronization of DC energy storage using MATLAB Simulink® environment. The considered power conditioning device is a Voltage Source Inverter (VSI), which is controlled using two methods called (i) traditional Zero Crossing Detection (ZCD) technique and (ii) the phase detector based Phase Locked Loop (PLL) technique. This work provides a comparative study of these two techniques with power quality/total harmonic distortions using low pas filter configuration.

Botnet attacks in Internet of Things (IoT) environments often occur as multi-stage campaigns, making early and reliable detection difficult across distributed and privacy-sensitive networks. Centralized detection approaches are often limited by heterogeneous traffic characteristics, severe data imbalance, and the need to aggregate large volumes of raw network data, raising scalability and privacy concerns. To address these challenges, this paper proposes FDA, a federated learning-based and data mining-driven framework for stage-aware botnet attack detection in IoT networks. FDA operates at network gateways, where anomalous traffic is first detected and then abstracted into compact and interpretable patterns using Frequent Itemset Mining (FIM). This pattern-based representation reduces noise and local traffic bias, enabling more robust learning across different IoT networks. Lightweight neural network models are trained locally at gateways, and a global model is learned through federated aggregation of model parameters, avoiding direct sharing of raw network data while enabling gateways to collaboratively learn evolving attack patterns across different IoT networks. Experimental results show that FDA achieves anomaly detection F1-scores above 99% across all gateways and multi-stage botnet attack classification F1-scores in the range of 48–49%, which are comparable to centralized machine-learning baselines while operating under decentralized and privacy-preserving constraints. Overall, FDA provides a practical, privacy-preserving, and effective solution for distributed botnet attack stage detection in real-world IoT deployments.

The looming energy crisis is becoming the greatest challenge to the world today. As a consequence of this there is an increasing attention for alternative renewable energy resources. In future, renewable energy sources will be integrated with energy storage and it will require a proper converter for integrating with the grid. In order to achieve this, various power electronics topologies have to be incorporated between the grid and the renewable source(s). This paper presents the simulation and experimental implementation of DC power source integration in micro-grid using single phase `Voltage Source Inverter' (VSI) with `Phase Locked Loop' (PLL) topology. Firstly the appropriateness of the PLL technique for the micro-grid synchronization is analyzed under the normal condition, harmonic condition and the non-harmonic (e.g. impulse) condition of a power system using Matlab Simulink®. Then the design and experimental implementation steps of the grid-tied VSI are discussed in details. In this work, a VSI with PLL technique is presented with simulation and experimental results for integrating with 230V/50Hz low voltage grid.