seLEEN

seLEEN, short for Self-learning Environmental Efficiency Engine and Network, is a modular software platform designed to monitor, analyze, and optimize energy use and environmental impact across organizations and urban systems. It combines data collection from sensors and external sources with machine learning models to produce actionable insights, forecasts, and optimization strategies. The platform is interoperable, supports open standards, and can be deployed on premises or in the cloud.

Origin and development: The concept emerged from academic and industry collaborations in the late 2010s, with a consortium releasing a core open-source edition and an enterprise edition. The project emphasizes transparency, reproducibility, and governance by a stewarding body that manages licensing and community contributions.

Its architecture consists of a data ingestion layer, a data lake, a model library, an optimization engine, and a visualization and reporting layer. It supports batch and real-time processing, APIs for integration, and privacy-preserving analytics, and can incorporate weather data to improve predictions.

Key features include anomaly detection, demand forecasting, energy optimization, carbon accounting, scenario planning, and automated reporting. A plugin ecosystem enables researchers and vendors to extend the platform with domain-specific modules, while a focus on auditability and explainability supports governance.

Applications include commercial buildings, manufacturing plants, campus networks, microgrids, and urban energy planning, as well as utilities modeling demand response and distributed resources.

Adoption has highlighted value in data-rich contexts but also challenges such as data quality, interoperability, and change management. Proponents argue seLEEN can accelerate efficiency and decarbonization when implemented with solid governance and stakeholder engagement.

See also related topics include energy management software, building automation, and open data standards.