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Abstract:The aim of this study was to develop an artificial neural network (ANN) prediction model for controlling building heating systems. This model was used to calculate the ascent time of indoor temperature from the setback period (when a building was not occupied) to a target setpoint temperature (when a building was occupied). The calculated ascent time was applied to determine the proper moment to start increasing the temperature from the setback temperature to reach the target temperature at an appropriate time. Three major steps were conducted: (1) model development; (2) model optimization; and (3) performance evaluation. Two software programs-Matrix Laboratory (MATLAB) and Transient Systems Simulation (TRNSYS)-were used for model development, performance tests, and numerical simulation methods. Correlation analysis between input variables and the output variable of the ANN model revealed that two input variables (current indoor air temperature and temperature difference from the target setpoint temperature), presented relatively strong relationships with the ascent time to the target setpoint temperature. These two variables were used as input neurons. Analyzing the difference between the simulated and predicted values from the ANN model provided the optimal number of hidden neurons (9), hidden layers (3), moment (0.9), and learning rate (0.9). At the study's conclusion, the optimized model proved its prediction accuracy with acceptable errors.

This Dataset provides a method of optimizing robot arm, facade pick and place locations in the construction site during facade assembly activity using generative design. A set of generative algorithms are provided in the form of graphical algorithm editors. The dataset is divided into three sets, each set controlling an essential subtask of facade assembly in the construction site. the dataset is called (iFOBOT) and consist of the following sub datasets: generative tool for facade population on building envelop (iFOBOT-D), Generative algorithm aided robot spatial location optimizer (iFOBOT-B), and Quantity take-off generative (iFOBOT-L). A sample project associated with its script and outcome results are included in this dataset to guide readers how to use this tool. This dataset only focuses on robot arm and facade module placement in construction sites. This dataset can generate optimized location of robot arm workstation in jobsite while also reducing robot collision with its body and surrounding objects, 2) reducing reachability rate, 3) reducing robot time travel during operation which in result minimize risk in facade assembly and increase productivity. This dataset is in parametric format which makes it reusable with all its history data using the reproducing guide provided here. More details of how to reuse this dataset and developed tool in construction site is covered in Robot-based Facade Spatial Assembly Optimization paper [1] .