Non-intrusive load monitoring (NILM) has emerged as a pivotal technology in energy management applications by enabling precise monitoring of individual appliance energy consumption without the requirements of intrusive sensors or smart meters. In this technique, the load disaggregation for the individual device is accrued by the recognition of their current signals by employing machine learning (ML) methods. This research paper conducts a comprehensive comparative analysis of various ML techniques applied to NILM, aiming to identify the most effective methodologies for accurate load disaggregation. The study employs a diverse dataset comprising high-resolution electricity consumption data collected from an Estonian household. The ML algorithms, including deep neural networks based on long short-term memory networks (LSTM), extreme gradient boost (XgBoost), logistic regression (LR), and dynamic time warping with K-nearest neighbor (DTW-KNN) are implemented and evaluated for their performance in load disaggregation. Key evaluation metrics such as accuracy, precision, recall, and F1 score are utilized to assess the effectiveness of each technique in capturing the nuanced energy consumption patterns of diverse appliances. Results indicate that the XgBoost-based model demonstrates superior performance in accurately identifying and disaggregating individual loads from aggregated energy consumption data. Insights derived from this research contribute to the optimization of NILM techniques for real-world applications, facilitating enhanced energy efficiency and informed decision-making in smart grid environments.

The burgeoning trend of integrating renewable energy harvesters into the grid introduces critical issues for its reliability and stability. These issues arise from the stochastic and intermittent nature of renewable energy sources. Data-driven forecasting tools are indispensable in mitigating these challenges with their rugged performance. However, tools relying solely on data-driven methods often underperform when an adequate amount of recorded data is unattainable. To bridge this gap, this paper presents a novel day-ahead hybrid forecasting framework for photovoltaic applications. This framework integrates a physics-based model with Machine Learning (ML) techniques, enhancing prediction reliability in environments with scarce data. Additionally, an innovative ML pipeline is introduced for data-abundant environments. The proposed ML tool comprises two branches: a set of regressors, each tailored for specific weather conditions, and a self-attention-based encoder-decoder network. By fusing the outputs from these branches through a meta-learner, the tool achieves predictions of higher quality, as evidenced by its superior performance over benchmark models in an investigated test dataset.