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About

MPCPy is a Python package that facilitates the testing and implementation of occupant-integrated model predictive control (MPC) for building systems. The package focuses on the use of data-driven, simplified physical or statistical models to predict building performance and optimize control. Four main modules contain object classes to import data, interact with real or emulated systems, estimate and validate data-driven models, and optimize control input. While MPCPy provides an integration platform, it relies on free, open-source, third-party software packages for model implementation, simulators, parameter estimation algorithms, and optimization solvers. This includes Python packages for scripting and data manipulation as well as other more comprehensive software packages for specific purposes. In particular, modeling and optimization for physical systems currently rely on the Modelica language specification.

About

Train in Data is your go-to online school for mastering machine learning. We offer intermediate and advanced courses in Python programming, data science and machine learning, taught by industry experts with extensive experience in developing, optimizing, and deploying machine learning models in enterprise production environments. We focus on building a solid, intuitive grasp of machine learning concepts, backed by hands-on Python coding to make sure you can actually apply what you learn. Our approach? Simple: learn the theory, understand the why behind it, then get coding. We give you the complete package—theory, coding, and troubleshooting skills—so you can confidently handle real-world projects from start to finish.

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Supported
Cloud Not Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Platforms Supported

Windows Supported
Mac Supported
Linux Not Supported
Cloud Not Supported
On-Premises Not Supported
iPhone Supported
iPad Supported
Android Not Supported
Chromebook Not Supported

Audience

Plants and companies requiring an open-source platform to improve their Model Predictive Control (MPC) in their buildings

Audience

Train in Data is ideal for intermediate to advanced learners seeking practical, expert-led machine learning and Python training to confidently tackle real-world projects

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

API

Offers API Supported

API

Offers API Not Supported

Screenshots and Videos

Screenshots and Videos

Pricing

Free
Free Version Supported
Free Trial Not Supported

Pricing

$15
Free Version Not Supported
Free Trial Not Supported

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 4.8 / 5
ease 4.8 / 5
features 4.8 / 5
design 4.7 / 5
support 4.6 / 5

Pros & Cons from Real Users

Pros

  • It is very good content mostly for the time series, machine learning, good material and up to date and advanced materials
  • I like the way they explain and teach the concept. The videos are easy to digest and helps learners retain information
  • The Forecasting with Machine Learning course was very useful for my final year thesis. Loved the examples with respect to retail. The python notebooks are available to follow along. Am not trying to implement this at work for promotion planning. The interface is friendly and it's possible to take notes. References to other useful material provided. Instructors further videos on Youtube were useful too.
  • The feature engineering course is very thorough, it’s well organized and practical. I would recommend is to everyone interested in ML engineering
  • I really liked the intuitive interface and the wide range of features available. The platform makes data management and analysis much easier, and the customer support team has always been quick and helpful whenever I had questions.

Cons

  • it would be better to more focus on this topic and the application of time series foundation models that are now very popular, it would be much more efficient to cover a variety of topics in this area, moreover I believe it can be good if they talk more about the Gen AI type of models
  • I would like to have more exercise and diverse projects to work on.
  • Need some understanding of forecasting and machine learning. This is not meant for complete beginners. This isn't really a con but FYI.
  • I think the intention of the course is to serve as a reference for future projects. Maybe it would be interesting to have some follow up courses based on specific full projects
  • The only downside is that it can take a bit of time to learn all the advanced options, but once you get used to it, the experience is excellent.

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Company Information

MPCPy
United States
github.com/lbl-srg/MPCPy

Company Information

Train in Data
Founded: 2019
Germany
trainindata.com

Alternatives

Cybernetica CENIT

Cybernetica CENIT

Cybernetica

Alternatives

COLUMBO

COLUMBO

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INCA MPC

INCA MPC

Inca Tools
AVEVA APC

AVEVA APC

AVEVA
Kaggle

Kaggle

Google

Categories

Categories

Integrations

Python Supported
Jupyter Notebook Not Supported
JupyterHub Not Supported
Matplotlib Not Supported
NumPy Not Supported
Ubuntu Supported
pandas Not Supported
scikit-learn Not Supported

Integrations

Python Supported
Jupyter Notebook Supported
JupyterHub Supported
Matplotlib Supported
NumPy Supported
Ubuntu Not Supported
pandas Supported
scikit-learn Supported
Claim MPCPy and update features and information
Claim MPCPy and update features and information
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