Smart-meter technology advancements have resulted in the generation of massive volumes of information introducing new opportunities for energy services and data-driven business models. One such service is non-intrusive load monitoring (NILM). NILM is a process to break down the electricity consumption on an appliance level by analyzing the total aggregated data measurements monitored from a single point. Most prominent existing solutions use deep learning techniques resulting in models with millions of parameters and a high computational burden. Some of these solutions use the turn-on transient response of the target appliance to calculate its energy consumption, while others require the total operation cycle. In the latter case, disaggregation is performed either with delay (in the order of minutes) or only for past events. In this paper, a real-time NILM system is proposed. The scope of the proposed NILM algorithm is to detect the turning-on of a target appliance by processing the measured active power transient response and estimate its consumption in real-time. The proposed system consists of three main blocks, i.e., an event detection algorithm, a convolutional neural network classifier and a power estimation algorithm. Experimental results reveal that the proposed system can achieve promising results in real-time, presenting high computational and memory efficiency.

Despite the rapid expansion of smart grids and large volumes of data at the individual consumer level, there are still various cases where adequate data collection to train accurate load forecasting models is challenging or even impossible. This paper proposes adapting an established Model-Agnostic Meta-Learning algorithm for short-term load forecasting in the context of few-shot learning. Specifically, the proposed method can rapidly adapt and generalize within any unknown load time series of arbitrary length using only minimal training samples. In this context, the meta-learning model learns an optimal set of initial parameters for a base-level learner recurrent neural network. The proposed model is evaluated using a dataset of historical load consumption data from real-world consumers. Despite the examined load series’ short length, it produces accurate forecasts outperforming transfer learning and task-specific machine learning methods by $12.5\%$. To enhance robustness and fairness during model evaluation, a novel metric, mean average log percentage error, is proposed that alleviates the bias introduced by the commonly used MAPE metric. Finally, a series of studies to evaluate the model’s robustness under different hyperparameters and time series lengths is also conducted, demonstrating that the proposed approach consistently outperforms all other models.

This paper introduces a holistic, multi-objective home energy management system (HEMS) designed to optimize residential electrical and thermal demands. By incorporating dynamic electricity tariffs and integrating solar production with storage units, the system aims to minimize energy costs while ensuring thermal comfort through energy-efficient solutions. Environmental sustainability and the impact on the distribution grid are also considered within the formulated optimization problem. A non-intrusive load monitoring tool is employed to schedule the operation of flexible appliances tailored to user habits, achieving a mean absolute error below 14W and an F1 score exceeding 0.8 for the most energy-intensive appliances across three public datasets. Analysis of five operating scenarios highlights the impact of each objective on system performance. The proposed HEMS outperforms baseline solutions, substantially reducing operational and environmental costs up to 42% and 27%, respectively. Moreover, it diminishes user discomfort by up to 86% and alleviates stress on the grid by 33%.

Non-intrusive load monitoring (NILM) is a topic that lately attracts both the academic and the industrial interest. NILM is used to reveal useful information regarding the consumption breakdown on appliance or activity level, thus can be a key solution to unlock various smart-home services and opportunities. To that end, deep learning has arisen as a prominent solution. Although most of the known NILM techniques so far focus on a predefined number of home appliances, this paper proposes a system which can detect in real-time any number of appliances. In the proposed solution switched-on appliances are identified by processing the measured active power transient response sampled at 100 Hz. The NILM system includes three stages; statistics-based event detection, convolutional neural network and k-nearest neighbors classifier. For future extensions, it is capable to automatically identify new appliances; thus, no retraining and additional modeling is required.