Modeling of Hybrid Model Predictive Control using MATLAB & Energy Plus Co Simulation
Step 1: Initially, we will collect and load building simulation data using EnergyPlus–Simulink co-simulation with weather inputs such as outdoor temperature, humidity and other simulation data.
Step 2: Then, we will preprocess the collected data to remove inconsistencies and analyze indoor temperature and humidity behavior.
Step 3: Next, we will predict future indoor temperature using the linear thermal model technique using RC network modeling based on collected data.
Step 4: Next, we implement the nonlinear error prediction model using NARX to capture complex effects like humidity, and wind disturbances based on collected data.
Step 5: Next, we integrate both models to form a hybrid predictive model by combining linear model output with NARX-based error correction based on collected data.
Step 6: Next, we implement a hybrid MPC controller using an optimization function with constraints and analyze the energy consumption and indoor comfort based on collected data.
Step 7: Finally, we evaluate and plot performance metrics for the following:
7.1: Time vs Indoor Temperature (°C)
7.2: Time vs Indoor Humidity (%)
7.3: Time vs Energy Consumption (J)
7.4: Time vs Indoor Comfort
1. Development Tool: MATLAB R2023a (or above version) with Energy Plus2. Operating System: Windows 10 (64-bit) or above
Note:
1) If the proposed plan does not fully align with your requirements, please provide all necessary details—including steps, parameters, models, and expected outcomes—in advance.2) Kindly ensure that any missing configurations or specifications are clearly outlined in the plan before confirming.3) If there’s no built-in solution for what the project needs, we can always turn to reference models, customize our own, different math models or write the code ourselves to fulfil the process.4) If the plan satisfies your requirement, Please confirm with us.5) Project based on Simulation only.
