Goptimise is a sophisticated cloud-based platform designed to help companies control and optimize their energy assets. By utilizing advanced analytics and data-driven insights, the platform enables businesses to streamline energy consumption, reducing costs and maximizing profit margins. With features like real-time monitoring and automated reporting, Goptimise offers comprehensive solutions tailored to meet the unique needs of different industries.

JADE-DR-VPP is an open-source Java framework that implements a multi-agent system for Virtual Power Plant (VPP) demand response (DR). Each agent represents a flexible load or generation unit that communicates via JADE messaging. The system orchestrates DR events, schedules load adjustments, and aggregates resources to meet grid signals. Users can configure agent behaviors, run large-scale simulations, and analyze performance metrics for energy management strategies.

Devath is an innovative AI-driven smart home system designed to elevate your home automation experience. It integrates effortlessly with various smart devices, allowing users to control and automate their home environment through a user-friendly interface. With Devath, users can create customized scenarios, manage energy efficiency, and enhance overall convenience in their daily lives – all without needing any technical expertise or coding knowledge.

Toyota Woven City is a visionary project that integrates cutting-edge AI technologies within a fully connected city environment. This project aims to optimize urban living by focusing on smart mobility solutions, efficient energy management, and seamless integration of automated systems. AI agents in Woven City enhance transportation systems, facilitate automated services, and monitor energy consumption, providing residents with a futuristic and sustainable lifestyle experience.

CityLearn provides a modular simulation platform for energy management research using reinforcement learning. Users can define multi-zone building clusters, configure HVAC systems, storage units, and renewable sources, then train RL agents against demand response events. The environment exposes state observations like temperatures, load profiles, and energy prices, while actions control setpoints and storage dispatch. A flexible reward API allows custom metrics—such as cost savings or emission reductions—and logging utilities support performance analysis. CityLearn is ideal for benchmarking, curriculum learning, and developing novel control strategies in a reproducible research framework.

Fetch.ai is an open-source platform and software development kit designed for building autonomous agents that represent digital twins on a decentralized network. It provides a Python and Rust SDK, an Open Economic Framework (OEF) for peer discovery, and seamless integration with its ledger for secure transactions. Developers can define custom agent skills—such as market making, data provision, or task bidding—and deploy them to testnets or mainnets. Fetch.ai agents autonomously communicate, negotiate, and execute smart contracts, enabling powerful multi-agent coordination for supply chains, IoT ecosystems, mobility services, energy grids, and beyond.