Non-intrusive load monitoring (NILM) addresses the challenging task of decomposing the aggregate signal of a household's electricity consumption into appliance-level data without installing dedicated meters. By detecting load malfunction and recommending energy reduction programs, cost-effective NILM provides intelligent demand-side management for utilities and end users. In this paper, we boost the accuracy of energy disaggregation with a novel neural network structure named scale- and context-aware network (SCANet), which exploits multi-scale features and contextual information. Specifically, we develop a multi-branch architecture with multiple receptive field sizes and branch-wise gates that connect the branches in the sub-networks. We build a self-attention module to facilitate the integration of global context. We incorporate an adversarial loss and on-state augmentation to further improve the model's performance. Extensive simulation results tested on open datasets corroborate the merits of the proposed approach, which significantly outperforms state-of-the-art methods.

Distributed photovoltaic power generation is volatile and intermittent, and its power generation is usually difficult to accurately predict. Previous studies have focused on physical or mathematical modeling methods, and it is difficult to grasp the complexity and variability of historical data, and the prediction accuracy is limited. To address these challenges, this paper proposes Solar-LLM, a novel prediction framework that adapts a pre-trained Large Language Model (LLM) for time-series forecasting. By freezing the core LLM and reprogramming only its input and output layers, Solar-LLM efficiently translates numerical time-series data into a format the model can understand. This approach leverages the LLM’s inherent ability to capture long-term dependencies and complex patterns, enabling effective learning even from limited data. Experiments conducted on a dataset from five photovoltaic power stations show that Solar-LLM significantly outperforms baseline models, proving it to be a highly effective and feasible solution for distributed PV power prediction.