Generating synthetic residential load data that can accurately represent actual electricity consumption patterns is crucial for effective power system planning and operation. The necessity for synthetic data is underscored by the inherent challenges associated with using real-world load data, such as privacy considerations and logistical complexities in large-scale data collection. In this work, we tackle the above-mentioned challenges by developing the Ensemble Recurrent Generative Adversarial Network (ERGAN) framework to generate high-fidelity synthetic residential load data. ERGAN leverages an ensemble of recurrent Generative Adversarial Networks, augmented by a loss function that concurrently takes into account adversarial loss and differences between statistical properties. Our developed ERGAN can capture diverse load patterns across various households, thereby enhancing the realism and diversity of the synthetic data generated. Comprehensive evaluations demonstrate that our method consistently outperforms established benchmarks in the synthetic generation of residential load data across various performance metrics including diversity, similarity, and statistical measures. The findings confirm the potential of ERGAN as an effective tool for energy applications requiring synthetic yet realistic load data. We also make the generated synthetic residential load patterns publicly available.

Smart meters play a crucial role in enhancing energy management and efficiency, but they raise significant privacy concerns by potentially revealing detailed user behaviors through energy consumption patterns. Recent scholarly efforts have focused on developing battery-aided load-shaping techniques to protect user privacy while balancing costs. This paper proposes a novel deep reinforcement learning-based load-shaping algorithm (PLS-DQN) designed to protect user privacy by proactively creating artificial load signatures that mislead potential attackers. We evaluate our proposed algorithm against a non-intrusive load monitoring (NILM) adversary. The results demonstrate that our approach not only effectively conceals real energy usage patterns but also outperforms state-of-the-art methods in enhancing user privacy while maintaining cost efficiency.

We present a framework for achieving end-to-end management objectives for multiple services that concurrently execute on a service mesh. We apply reinforcement learning (RL) techniques to train an agent that periodically performs control actions to reallocate resources. We develop and evaluate the framework using a laboratory testbed where we run information and computing services on a service mesh, supported by the Istio and Kubernetes platforms. We investigate different management objectives that include end-to-end delay bounds on service requests, throughput objectives, cost-related objectives, and service differentiation. We compute the control policies on a simulator rather than on the testbed, which speeds up the training time by orders of magnitude for the scenarios we study. Our proposed framework is novel in that it advocates a top-down approach whereby the management objectives are defined first and then mapped onto the available control actions. It allows us to execute several types of control actions simultaneously. By first learning the system model and the operating region from testbed traces, we can train the agent for different management objectives in parallel.

The computation burden of solving nonlinear optimization Recent years have seen a rich literature of data-driven approaches problems in operation and planning has motivated the designed for power grid applications. However, development of data-driven alternatives to state estimation insufficient consideration of domain knowledge can impose (SE) [7] [19], power flow (PF) analysis [4][14], optimal power a high risk to the practicality of the methods. Specifically, flow (OPF) [2] [9][6], as well as data-driven warm starters ignoring the grid-specific spatiotemporal patterns (in load, to collaborate with physical solvers [12][20], etc. generation, and topology, etc.) can lead to outputting infeasible, Despite their popularity in recent years, people have long unrealizable, or completely meaningless predictions on been aware of the risks of machine learning (ML) tools regarding new inputs. To address this concern, this paper investigates their impracticality[11] under realistic power grid real-world operational data to provide insights into power conditions. The risks come from the "missing of physics" in grid behavioral patterns, including the time-varying topology, general ML methods. Specifically, the transient system dynamics, load, and generation, as well as the spatial differences changing topology, and varying supply and demand (in peak hours, diverse styles) between individual loads and are physical reasons behind the temporal grid evolution.

We present a framework that lets a service provider achieve end-to-end management objectives under varying load. Dynamic control actions are performed by a reinforcement learning (RL) agent. Our work includes experimentation and evaluation on a laboratory testbed where we have implemented basic information services on a service mesh supported by the Istio and Kubernetes platforms. We investigate different management objectives that include end-to-end delay bounds on service requests, throughput objectives, and service differentiation. These objectives are mapped onto reward functions that an RL agent learns to optimize, by executing control actions, namely, request routing and request blocking. We compute the control policies not on the testbed, but in a simulator, which speeds up the learning process by orders of magnitude. In our approach, the system model is learned on the testbed; it is then used to instantiate the simulator, which produces near-optimal control policies for various management objectives. The learned policies are then evaluated on the testbed using unseen load patterns.

This paper models residential consumers' energy-consumption behavior by load patterns and distributions and reveals the relationship between consumers' load patterns and socioeconomic features by machine learning. We analyze the real-world smart meter data and extract load patterns using K-Medoids clustering, which is robust to outliers. We develop an analytical framework with feature selection and deep learning models to estimate the relationship between load patterns and socioeconomic features. Specifically, we use an entropy-based feature selection method to identify the critical socioeconomic characteristics that affect load patterns and benefit our method's interpretability. We further develop a customized deep neural network model to characterize the relationship between consumers' load patterns and selected socioeconomic features. Numerical studies validate our proposed framework using Pecan Street smart meter data and survey. We demonstrate that our framework can capture the relationship between load patterns and socioeconomic information and outperform benchmarks such as regression and single DNN models.

Accurately predicting the future health of batteries is necessaryElectrochemical batteries, such as lithium-ion and leadacid to ensure reliable operation, minimise maintenance cells, experience degradation over time and during costs, and calculate the value of energy storage investments.usage, leading to decreased energy storage capacity and The complex nature of degradation renders datadrivenincreased internal resistance. Being able to predict the approaches a promising alternative to mechanistic rate of degradation and the remaining useful life (RUL) modelling. This study predicts the changes in batteryof a battery is important for performance and economic capacity over time using a Bayesian nonparametric reasons. For example, in an electric vehicle, the driveable approach based on Gaussian process regression. These range is directly related to the battery capacity. For energy changes can be integrated against an arbitrary input sequence storage asset valuation, depreciation, warranty, insurance to predict capacity fade in a variety of usage scenarios, and preventative maintenance purposes, predicting forming a generalised health model. The approach RUL at design stage and during operation is crucial, and naturally incorporates varying current, voltage and temperaturethe investment case is strongly dependent on the degradation inputs, crucial for enabling real world application.