Search

Accurate short-term forecasting of household electricity consumption is vital to contemporary distributed energy networks. Most approaches to load prediction rely on deep neural architectures and require access to confidential, user-specific consumption records during training. Traditional neural network training typically collects all available data at a single central location to develop one unified model. Nevertheless, pooling individual user information in this manner raises serious concerns regarding data security and personal privacy. To address these issues, the present research explores federated learning techniques combined with model personalization as promising alternatives. Through a comprehensive simulation-based evaluation, these approaches are benchmarked against the standard centralized training strategy and a simple pre-trained reference model. The analysis reveals that the intrinsic characteristics and heterogeneity of the underlying electricity consumption patterns significantly affect the resulting model error levels. Consequently, directly contrasting error distributions across methods largely reflects differences in data composition rather than true differences in algorithmic effectiveness. To overcome this limitation, the study introduces a novel evaluation technique termed “differential comparison.” This approach focuses exclusively on pairwise loss differences across identical data instances, thereby enabling a fairer assessment of relative model improvements via the resulting difference distributions. Applying this method demonstrates that personalized model training outperforms all other strategies examined for residential load prediction, achieving better results in 59.1% of the evaluated scenarios.

Modern smart grids aim to deliver exceptional reliability and operational flexibility in power delivery. Contemporary electrical networks rely on synchronized wide-area monitoring tools, notably Phasor Measurement Units (PMUs), to supply instantaneous system status updates. These devices support prompt problem identification and corrective actions while ensuring uninterrupted grid operation. The surge in available measurement data has accelerated the adoption of machine learning techniques for tasks such as power system protection and state estimation. This work presents an intelligent fault detection and classification (FD&C) framework for transmission lines that employs an auto-encoder neural network architecture. The study evaluates and contrasts the capabilities of a Multi-Layer Extreme Learning Machine (ML-ELM) with those of a Stacked Auto-Encoder (SAE) neural network. These models are further benchmarked against established classifier approaches that rely on wavelet-based feature extraction, as well as against various other deep learning strategies. The proposed auto-encoder solutions attain perfect (100%) fault detection rates and achieve fault classification accuracies of 99.92% and 99.79%, all while demanding far less computation time during the testing phase. The ML-ELM variant stands out for its efficiency, maintaining excellent classification performance with both training and testing durations below 50 milliseconds. To reflect practical field conditions, the models were rigorously tested on datasets incorporating additive noise levels corresponding to signal-to-noise ratios (SNR) between 10 dB and 40 dB. Validation was performed using simulated data from the IEEE 39-bus benchmark system.

Advancements in technology, continuous innovation, and the emergence of the Industry 4.0 paradigm are driving Distribution System Operators toward increasingly competitive environments where flexible demand response mechanisms are essential. In many developing nations, however, the absence of standardized measurement frameworks across industrial process chains hinders the effective deployment of demand-side management models, leading to inefficiencies in data analysis and limiting the accurate assessment of large-scale consumers’ contributions to flexibility. To address this gap, the present study utilizes load profile data—specifically energy and power measurements obtained from utility meters—as input variables to generate disaggregated forecasts at 15-minute intervals across four defined time segments. The proposed approach is validated through performance metrics, with outcomes assessed using the Root Mean Square Error (RMSE) to quantify total methodological error. The framework integrates Machine Learning and Big Data techniques implemented in Python, employing Combinatorial Disaggregation Optimization alongside Factorial Hidden Markov Models to enhance prediction accuracy.