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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 INCA MPC?

Advanced Process Control (APC) serves as an effective means to boost your plant's operational efficiency without necessitating any changes to hardware. By deploying an APC application, you can achieve stable operations while optimizing either production output or energy consumption, which in turn grants valuable insights into your manufacturing workflows. This concept includes a diverse set of methodologies and technologies that enhance basic process control systems, which are mainly built upon PID controllers. For instance, technologies associated with APC feature LQR, LQC, H_infinity, neural networks, fuzzy logic, and Model-Based Predictive Control (MPC). An APC application works tirelessly to optimize plant performance every minute of every day, ensuring that operational efficiency remains consistent. Among these different techniques, MPC is particularly prominent in the industry because it employs a process model to anticipate the plant's performance in the near term, usually from a few minutes to several hours ahead, thereby offering a tactical edge in operational management. By continuously refining processes, APC not only enhances immediate efficiency but also plays a crucial role in achieving long-term sustainability objectives, making it an essential component of modern industrial practices. The integration of these advanced strategies can lead to significant improvements in both productivity and environmental impact.

Media

Media

Integrations Supported

Python
Ubuntu

Integrations Supported

Python
Ubuntu

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

Inca Tools

Company Location

Netherlands

Company Website

www.incatools.com/advanced-process-control/

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