This article addresses the suitable approaches for empowering energy citizens and smart energy communities through the development of community-based microgrid (C-MG) solutions while taking into consideration the functional architectural layers and system integration topologies, interoperability issues, strategies for consumer-centric energy trading under the local electricity market (LEM) mechanism, and socio-economic aspects. Thus, this article presents state-of-the-art microgrid solutions for the smart energy community along with their motivation, advantages and challenges, comprehensibly contrasted between the recommended generic architecture and every other reported structure. The notion of LEM for peer-to-peer (P2P) energy exchange inside a transactive energy system based on a flexible consumer-centric and bottom-up perspective towards the participation in the wholesale electricity market (WEM) is also reviewed and critically explored. Furthermore, the article reviews the interoperability issues in relation to the development of C-MG including energy trading facilities. The article’s overall contribution is that it paves the path for advanced research and industrialisation in the field of smart energy communities through the analytical recommendations of the C-MG architecture and DER (distributed energy resource) integration structure, considering the future trend of local energy markets and socio-economic aspects.

The data-driven (DD) is a systematic approach to improve the data and model by deriving/adding features to address the problem identified during the iterative loop of forecasting model development. This article proposes a DD framework for forecasting short-term PV generation and load demand. A framework of three stages with a unique contribution in each stage, such as generalizing data preprocessing steps (stage-1), multivariate feature generation and selection (stage-2), and model hyperparameter tuning (stage-3) for further improvement in forecasting is proposed. It focuses on data as well as forecasting models. The whole process is analyzed using the time-series measured data collected from a real-life demonstration project in Ireland. Data preprocessing is generalized for both generation and demand forecasting under the same framework. The relevant features are selected with the help of the proposed random forest sequential forward feature selection algorithm. Hyperparameters are tuned through tree-structured Parzen estimator algorithm for further improvement. In addition, the performance of the classical autoregressive integrated moving average model is compared with the machine learning-based gate recurrent unit, long short term memory, recurrent neural network, and convolutional neural network models. Results show that the data-driven forecasting model framework systematically improves the model performance. The seasonal variation has also a high impact on the model performances.

This paper describes a comprehensive energy-related dataset, collected from residential electricity households within an energy community in Ireland, as part of StoreNet project. The data includes local weather parameters and per household power (W) and energy (Wh) components for various aspects such as active power consumption, PV generation, grid import and export, charging and discharging, and the state of charge of energy storage. Additionally, it provides weather data for the location at a 1-minute temporal resolution for the year 2020. The dataset has been validated by comparing measurements that should yield identical results to standard load profiles, with no significant inconsistencies discovered. Validation examples have also been given through the published articles where this dataset has been used to analyse peer-to-peer energy trading benefits for the energy community and decision-making support for aggregators. The dataset aids in understanding patterns in electrical load curves and the duty cycle of energy storage within an energy community. Furthermore, it can assist in comprehending the impact on distribution networks caused by distributed energy storage.