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Description
MPCPy is a Python library designed to support the testing and execution of occupant-integrated model predictive control (MPC) within building systems. This tool emphasizes the application of data-driven, simplified physical or statistical models to forecast building performance and enhance control strategies. It comprises four primary modules that provide object classes for data importation, interaction with real or simulated systems, data-driven model estimation and validation, and optimization of control inputs. Although MPCPy serves as a platform for integration, it depends on various free, open-source third-party software for model execution, simulation, parameter estimation techniques, and optimization solvers. This encompasses Python libraries for scripting and data manipulation, along with more specialized software solutions tailored for distinct tasks. Notably, the modeling and optimization tasks related to physical systems are currently grounded in the specifications of the Modelica language, which enhances the flexibility and capability of the package. In essence, MPCPy enables users to leverage advanced modeling techniques through a versatile and collaborative environment.
Description
Pitops stands out as the sole software solution capable of executing genuine closed-loop system identification with PID controllers in Auto mode, or even with secondary PID controllers in Cascade mode, without the necessity of interrupting the cascade chain or undertaking additional, labor-intensive plant step tests. No other competitor tool is capable of successfully identifying transfer functions using data from PID controllers in Cascade mode, making Pitops unparalleled in this regard. Additionally, Pitops conducts transfer function identification entirely in the time domain, unlike other tools that rely on the more intricate Laplace (S) or Discrete (Z) domains. It also has the remarkable ability to manage multiple inputs and identify several transfer functions simultaneously. Utilizing a groundbreaking proprietary algorithm, Pitops facilitates multiple inputs closed-loop transfer function system identification in the time domain, significantly surpassing traditional methods like ARX/ARMAX/Box and Jenkins used by competing tools. This innovative approach not only streamlines the process but also enhances accuracy and efficiency, making Pitops the preferred choice in the industry.
API Access
Has API
API Access
Has API
Integrations
Python
Ubuntu
Pricing Details
Free
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
MPCPy
Country
United States
Website
github.com/lbl-srg/MPCPy
Vendor Details
Company Name
PiControl Solutions
Founded
1992
Country
United States
Website
www.picontrolsolutions.com/products/pitops/