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What is MPCPy?

MPCPy is a Python-based library specifically created to facilitate the testing and implementation of occupant-integrated model predictive control (MPC) in building systems. This innovative tool focuses on utilizing data-driven, simplified physical or statistical models to predict the performance of buildings and improve control methodologies. It consists of four key modules that offer object classes for tasks such as data importation, engagement with either real or simulated systems, estimation and validation of data-driven models, and optimization of control inputs. While MPCPy acts as a comprehensive integration platform, it relies on a variety of free, open-source third-party software for executing models, conducting simulations, implementing parameter estimation techniques, and optimizing solvers. This includes Python libraries for scripting and data manipulation, as well as specialized software solutions designed for specific functions. Importantly, the tasks involving modeling and optimization of physical systems are currently based on the requirements of the Modelica language, which significantly enhances the package's flexibility and capabilities. Overall, MPCPy empowers users to harness sophisticated modeling methods within a dynamic and cooperative environment, ultimately fostering improved building system performance. Furthermore, it opens up opportunities for researchers and practitioners alike to experiment with cutting-edge control strategies tailored to real-world scenarios.

What is DIA?

DIA (Decentralised Information Asset) is an open-source oracle system designed to gather, distribute, and share trustworthy data among participants in the market. In the realm of decentralized finance (DeFi), having access to reliable and scalable data feeds is vital for the creation of robust products and for safeguarding against possible exploitation and manipulation. DIA employs crypto-economic incentives alongside community feedback to effectively gather, verify, and transmit credible financial information. Participants who assist in data sourcing and validation are rewarded with bounties paid in DIA tokens, encouraging active involvement. All data is collected from primary sources and sent to DIA's servers, where it is secured by hashing the database on-chain. Moreover, comprehensive scraper code and documentation are readily available on GitHub for public access. Users can retrieve this data via API endpoints or Oracles, which allows various entities like lending platforms, index providers, and prediction markets to utilize DIA’s open-source and validated data streams without any costs. This collaborative methodology not only improves data integrity but also promotes innovation and advancement within the DeFi landscape. Ultimately, DIA aims to create a more transparent and reliable financial ecosystem for all participants.

Media

Media

Integrations Supported

Moonbeam
Moonriver
Python
Ubuntu
Zeroswap

Integrations Supported

Moonbeam
Moonriver
Python
Ubuntu
Zeroswap

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

MPCPy

Company Location

United States

Company Website

github.com/lbl-srg/MPCPy

Company Facts

Organization Name

DIA Association

Date Founded

2018

Company Location

Switzerland

Company Website

diadata.org

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