Abstract
Simulations have been performed to analyze the performance metric characteristics in assessing the heating load energy of building shapes system based on the K-Nearest Neighbor (KNN) classifier. The system is implemented and simulated in Anaconda, and its performance is tested on a real dataset that contains 8 features and 768 instances to classify the heating load magnitude into four (04) classes (4 target name labels) created based on the captured load energy magnitude. The simulation results carried out under various setting parameters show that the performance metrics depend on the test size and k-neighbors, which gives better training accuracy, slightly higher than the test accuracy in the range of [85.20%–100%] and [79.20%–89.60%], respectively. For quality analysis, the present proposed methodology can serve as a test platform for measurement and verification of the energy heating load performance. It can be used as a performance metrics guideline that tells us how much better the proposed model is making a prediction.
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Boudjella, A., Boudjella, M.Y., Bellebna, M.E., Aoumeur, N., Belhouari, S. (2022). Prediction and Characterization of Heating Load Energy Performance of Residential Building Machine Learning Algorithms. In: Hatti, M. (eds) Artificial Intelligence and Heuristics for Smart Energy Efficiency in Smart Cities. IC-AIRES 2021. Lecture Notes in Networks and Systems, vol 361. Springer, Cham. https://doi.org/10.1007/978-3-030-92038-8_5
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