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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

Robyn is a cutting-edge, open-source Marketing Mix Modeling (MMM) tool created by Meta’s Marketing Science team for experimental purposes. It aims to assist advertisers and analysts in constructing thorough, data-driven models that assess how various marketing channels affect business results, such as sales and conversions, while ensuring privacy through aggregated data. Instead of depending on tracking individual users, Robyn delves into historical time-series data by integrating marketing expenditure or reach information—encompassing ads, promotions, and organic initiatives—with performance indicators to evaluate incremental impacts, saturation effects, and carry-over dynamics. The package utilizes a combination of classical statistical techniques and contemporary machine learning methods; it employs ridge regression to mitigate multicollinearity in complex models, performs time-series decomposition to differentiate between trends and seasonal patterns, and incorporates a multi-objective evolutionary algorithm for optimization. This innovative approach allows businesses to gain deeper insights into their marketing effectiveness and make more informed decisions based on robust analysis.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Facebook
Facebook Ads
Instagram Ads
Meta Ads
Meta Pixel
Python
Ubuntu

Integrations

Facebook
Facebook Ads
Instagram Ads
Meta Ads
Meta Pixel
Python
Ubuntu

Pricing Details

Free
Free Trial
Free Version

Pricing Details

Free
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

Meta

Founded

2004

Country

United States

Website

facebookexperimental.github.io/Robyn/

Product Features

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