To bridge the temporal granularity gap in energy network design and operation based on Energy System Models, resampling of time series is required. While conventional upsampling methods are computationally efficient, they often result in significant information loss or increased noise. Advanced models such as time series generation models, Super-Resolution models and imputation models show potential, but also face fundamental challenges. The goal of time series generative models is to learn the distribution of the original data to generate high-resolution series with similar statistical characteristics. This is not entirely consistent with the definition of upsampling. Time series Super-Resolution models or imputation models can degrade the accuracy of upsampling because the input low-resolution time series are sparse and may have insufficient context. Moreover, such models usually rely on supervised learning paradigms. This presents a fundamental application paradox: their training requires the high-resolution time series that is intrinsically absent in upsampling application scenarios. To address the mentioned upsampling issue, this paper introduces a new method utilizing Generative Adversarial Transformers (GATs), which can be trained without access to any ground-truth high-resolution data. Compared with conventional interpolation methods, the introduced method can reduce the root mean square error (RMSE) of upsampling tasks by 10%, and the accuracy of a model predictive control (MPC) application scenario is improved by 13%.

Advances in machine learning and increased computational power have driven progress in energy-related research. However, limited access to private energy data from buildings hinders traditional regression models relying on historical data. While generative models offer a solution, previous studies have primarily focused on short-term generation periods (e.g., daily profiles) and a limited number of meters. Thus, the study proposes a conditional diffusion model for generating high-quality synthetic energy data using relevant metadata. Using a dataset comprising 1,828 power meters from various buildings and countries, this model is compared with traditional methods like Conditional Generative Adversarial Networks (CGAN) and Conditional Variational Auto-Encoders (CVAE). It explicitly handles long-term annual consumption profiles, harnessing metadata such as location, weather, building, and meter type to produce coherent synthetic data that closely resembles real-world energy consumption patterns. The results demonstrate the proposed diffusion model's superior performance, with a 36% reduction in Frechet Inception Distance (FID) score and a 13% decrease in Kullback-Leibler divergence (KL divergence) compared to the following best method. The proposed method successfully generates high-quality energy data through metadata, and its code will be open-sourced, establishing a foundation for a broader array of energy data generation models in the future.

Privacy is important when dealing with sensitive personal information in machine learning models, which require large data sets for training. In the energy field, access to household prosumer energy data is crucial for energy predictions to support energy grid management and large-scale adoption of renewables however citizens are often hesitant to grant access to cloud-based machine learning models. Federated learning has been proposed as a solution to privacy challenges however report issues in generating the global prediction model due to data heterogeneity, variations in generation patterns, and the high number of parameters leading to even lower prediction accuracy. This paper addresses these challenges by introducing FedWOA a novel federated learning model that employs the Whale Optimization Algorithm to aggregate global prediction models from the weights of local LTSM neural network models trained on prosumer energy data. The proposed solution identifies the optimal vector of weights in the search spaces of the local models to construct the global shared model and then is subsequently transmitted to the local nodes to improve the prediction quality at the prosumer site while for handling non-IID data K-Means was used for clustering prosumers with similar scale of energy data. The evaluation results on prosumers energy data have shown that FedWOA can effectively enhance the accuracy of energy prediction models accuracy by 25% for MSE and 16% for MAE compared to FedAVG while demonstrating good convergence and reduced loss.

Among a variety of other considerations, energy efficiency is a major focus for the Union's ultimate decarbonization. This makes high energy efficiency a critical priority for all energy sectors, particularly the residential sector [2], which occupies more than a quarter of the Union's total final energy consumption. Energy decentralization has emerged as one of the most popular contemporary research topic in this domain as a mean for increasing energy efficiency [3]. With the growing usage of Information and Communication Technologies (ICT) in the Internet of Things (IoT) sector, data on household energy consumption and production (HECP) may now be generated in a decentralized manner, for example, from an electric vehicle, a heat pump, or home appliances. Due to the range and granularity of data-generating devices, a new generation of smart household energy systems is geared toward decentralization and has the potential to considerably assist in the transition to a sustainable energy future [4, 5]. On the other hand, evaluating household energy data is getting increasingly difficult as a result of various smart devices interacting and forming a complex energy flow data network [6, 7]. Decentralized energy systems are often paired with research into data-driven technologies (e.g. machine learning) for opti-2 mizing the systems based on the massive ocean of incoming data in order to manage the inherent risk associated with energy usage's intermittent and unpredictable nature and achieve energy sustainability, including cost reduction, emission reduction, and energy efficiency. However, most of those technologies are developed for project-specific decentralized data (i.e.

For many of us, our microwaves and dishwashers aren't the first thing that come to mind when trying to glean health information, beyond that we should (maybe) lay off the Hot Pockets and empty the dishes in a timely way. But we may soon be rethinking that, thanks to new research from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). The system, called "Sapple," analyzes in-home appliance usage to better understand our health patterns, using just radio signals and a smart electricity meter. Taking information from two in-home sensors, the new machine learning model examines use of everyday items like microwaves, stoves, and even hair dryers, and can detect where and when a particular appliance is being used. For example, for an elderly person living alone, learning appliance usage patterns could help their health-care professionals understand their ability to perform various activities of daily living, with the goal of eventually helping advise on healthy patterns.

We present a deep learning approach for vertex reconstruction of neutrino-nucleus interaction events, a problem in the domain of high energy physics. In this approach, we combine both energy and timing data that are collected in the MINERvA detector to perform classification and regression tasks. We show that the resulting network achieves higher accuracy than previous results while requiring a smaller model size and less training time. In particular, the proposed model outperforms the state-of-the-art by 4.00% on classification accuracy. For the regression task, our model achieves 0.9919 on the coefficient of determination, higher than the previous work (0.96).

Green technology has been prolific in the news recently with the UK's new industrial strategy demonstrating a step in the right direction towards embracing new technology in the energy space. There are plans in place to majorly change the way electricity is produced, used and stored, as the government becomes more aware of the need for a smarter, more flexible energy system. With UK consumers overpaying a staggering £5.4 billion a year on standard tariffs, it is time that the energy system is redesigned to optimise electricity prices and modernise the grid. As part of their strategy, the government has outlined plans to ensure that all households and businesses are given the option to have a smart meter installed. While these meters give the consumer greater control and a better understanding of their energy usage, the data made available to them only scratches the surface of what is actually possible and what can have a real impact on changing consumer behaviour.

We sat down for a quick chat with Safi Oranski, Head of Business Development at Panoramic Power, for some thoughts about analytics, machine learning and creating those "actionable insights" we all hear so much about. The digital and analytics revolution has finally come to the utilities industry. The old energy world where utilities simply provided electricity and gas to customers as a one way transaction are over. We are now at the forefront of a new energy world with intelligent appliances, connected devices and smarter grids, which means that customers now have much greater ability to control how much energy they use and when they use it. New new business models have resulted from today's environment of distributed generation, and smart grids.

Panoramic Power, a leading provider of device-level energy management solutions, is pleased to announce the release of Device Analyzer, an innovative data-science approach to energy management that generates actionable energy and operational efficiency insights. Using advanced machine learning algorithms, Device Analyzer provides users with greater visibility into the operational state of each of their monitored devices. Device Analyzer learns usage patterns for any device such as lighting, HVAC and production lines and with pre-defined algorithms per device-type, allows users to see the device's operational state in real-time. It understands and monitors device sequencing, detects anomalies and automatically generates operational insights. Device Analyzer shifts the user interaction from a focus only on energy consumption, to an operational one - delivering significant value to improve operations, productivity and facility performance.

Despite a dramatic growth of power consumption inhouseholds, less attention has been paid to monitoring,analyzing and predicting energy usage. In this paper,we propose a framework to mine raw energy data bytransforming time series energy data into a symbol se-quence, and then extend a suffix tree data structure asan efficient representation to analyze global structuralpatterns. Then, we use a clustering algorithm to detectenergy pattern outliers which are far from their clustercentroids. To validate our approach, we use real powerdata collected from a smart apartment testbed duringtwo months.