Energy
Use System Modeler to develop accurate models of wind, solar, and other power generation and distribution systems. Combine with Mathematica for further optimization and analysis.
Air Conditioning: Effect of Building Orientation
Which building properties affect the energy spent in air conditioning? This example uses data from 768 diverse residential buildings to predict their air conditioning’s thermal load by training a neural network. Connect this net with thermal modeling components to study the effect of building orientation.
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Preprocess Data
A dataset containing thermal load requirements based on building parameters such as compactness, glazing or orientation is used. The dataset is imported into the Wolfram Language and cleaned to make it suitable for model training.
Train a Neural Net
The data is split for training and validation. A neural net consisting of multiple layers is created, and all its learnable parameters are initialized.
Create a SystemModel
The trained neural net is converted to an input-output block that can be connected to other models.
Connect to the Building Model
The model is then attached to a single-room building model that consists of walls, roofs and an air conditioner. The connected model can be fed with external temperature data, and its energy performance can be tested.
Study Effect of Orientation
Change the orientation of the building and analyze the power consumed by the air conditioner. A building facing west has the lowest energy consumption, while a building facing north has the highest energy consumption.
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