Building EMS Energy Forecasting

With MATLAB and Simulink, you can perform data-driven forecasts on environmental and techno-economic factors to optimize operations of your building energy management system.

• Access time-series data from files or databases
• Perform data preprocessing, analysis, and visualization in MATLAB with interactive apps and workflow automations
• Choose from prebuilt statistical, econometric, or machine learning and deep learning models
• Train forecasting models in parallel and evaluate model performance
• Directly integrate the trained energy forecasting model in Simulink for simulation with the physical system model

Customer Story

• Modelling European Power Supply and demand with MATLAB and Parallel Computing

Building EMS Modeling, Simulation, and Optimization

You can use MATLAB and Simulink as a design environment for electrical system modeling, EMS control design, and EMS optimization.

• Create physics-based models of the building’s electrical system using Power Systems Simulation Onramp and run performance simulations
• Design control strategies, such as Model Predictive Control (MPC), for the EMS
• Formulate the optimization problem and solve for optimal system configurations using Optimization Toolbox
• Simulate system-level behavior with different scenarios
• Analyze the techno-economic impact of different system designs

Learn More

• BuildingIQ Develops Proactive Algorithms for HVAC Energy Optimization in Large-Scale Buildings – Customer Story
• EVLO Energy Storage Accelerates Development of Energy Management Systems with Model-Based Design – Customer Story
• Analytics Engine and Digital Twin Reveal Buildings’ Hidden Energy Costs – Newsletter
• Daikin Increases Development Efficiency Using Production Code Generation for Air Conditioner Controls – Customer Story

Building EMS Validation and Deployment

You can validate your building EMS design by generating code from your system model, which lets you move quickly from desktop simulation to real-time simulation. You can deploy controls to the edge and operational management systems to the cloud.

• Generate readable, efficient C/C++ code from the controller model in Simulink for deployment on an embedded processor
• Generate C code for the plant for deployment on a real-time machine
• Run hardware-in-the-loop (HIL) simulations to validate the building energy management strategies against the electrical system
• Deploy supervisory control algorithms on edge devices
• Deploy operations optimization algorithms based on MATLAB to cloud environments using MATLAB Web App Server and MATLAB Production Server

Customer Stories

• Murata Manufacturing Reduces Development Time for Energy Management System Control Software by More Than 50% with Model-Based Design
• Musashi Seimitsu Industry Reduces Development Time for Factory Energy Management System Through Model-Based Design