The first, and at the same time one of the most important tasks in building a predictive system is the task of analyzing the original data set. The next task is to process the data according to the purpose and objectives of the predictive system. These two tasks are often not given enough attention and, as a consequence, the developer does not get the desired result as a result of building the system. This paper discusses methods and algorithms for data analysis and processing that can help in building a predictive system. The data set from the machine learning competition "ASHRAE - Great Energy Predictor III" is used as input data. The analysis phase focuses on constructing graphs and drawing conclusions from them. The paper shows how to use graphical information to find anomalies in the data and eliminate them. Examples of graphs that provide information useful for building machine learning models are given. The data processing phase describes the transformations performed on the data. It considers the transformations that lead to positive results as well as those that lead to negative results.
Skip Nav Destination
Article navigation
12 January 2024
PROCEEDINGS OF THE III INTERNATIONAL CONFERENCE ON ADVANCES IN SCIENCE, ENGINEERING, AND DIGITAL EDUCATION: ASEDU-III 2022
8–10 December 2022
Krasnoyarsk, Russian Federation
Research Article|
January 12 2024
Analysis and preparation of a data set on energy consumption of buildings in machine learning models
P. Yu. Gusev;
P. Yu. Gusev
a)
1
Voronezh State Technical University
, 84, 20-letiya Oktyabrya str., Voronezh, 394006, Russian Federation
a)Corresponding author: [email protected]
Search for other works by this author on:
M. Yu. Chizhov;
M. Yu. Chizhov
1
Voronezh State Technical University
, 84, 20-letiya Oktyabrya str., Voronezh, 394006, Russian Federation
Search for other works by this author on:
A. D. Danilov;
A. D. Danilov
1
Voronezh State Technical University
, 84, 20-letiya Oktyabrya str., Voronezh, 394006, Russian Federation
Search for other works by this author on:
K. Yu. Gusev;
K. Yu. Gusev
1
Voronezh State Technical University
, 84, 20-letiya Oktyabrya str., Voronezh, 394006, Russian Federation
Search for other works by this author on:
O. V. Sobenina;
O. V. Sobenina
1
Voronezh State Technical University
, 84, 20-letiya Oktyabrya str., Voronezh, 394006, Russian Federation
Search for other works by this author on:
A. I. Kustov
A. I. Kustov
2
Voronezh Branch of Russian University of Economics Named after G.V. Plekhanov
, 67A, Karl Marx str., Voronezh, 394030, Russian Federation
Search for other works by this author on:
a)Corresponding author: [email protected]
AIP Conf. Proc. 2969, 050013 (2024)
Citation
P. Yu. Gusev, M. Yu. Chizhov, A. D. Danilov, K. Yu. Gusev, O. V. Sobenina, A. I. Kustov; Analysis and preparation of a data set on energy consumption of buildings in machine learning models. AIP Conf. Proc. 12 January 2024; 2969 (1): 050013. https://doi.org/10.1063/5.0181905
Download citation file:
Pay-Per-View Access
$40.00
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
29
Views
Citing articles via
Inkjet- and flextrail-printing of silicon polymer-based inks for local passivating contacts
Zohreh Kiaee, Andreas Lösel, et al.
Design of a 100 MW solar power plant on wetland in Bangladesh
Apu Kowsar, Sumon Chandra Debnath, et al.
Production and characterization of corncob biochar for agricultural use
Praphatsorn Rattanaphaiboon, Nigran Homdoung, et al.
Related Content
Exploring genetic algorithm to optimize hyper parameter for training of artificial neural network
AIP Conf. Proc. (November 2023)
A prediction of operating systems vulnerabilities using machine learning algorithms
AIP Conf. Proc. (May 2024)
Advancing fluid dynamics simulations: A comprehensive approach to optimizing physics-informed neural networks
Physics of Fluids (January 2024)