A Computational Intelligence Approach to Predict Energy Demand Using Random Forest in a Cloudera Cluster

Society’s energy consumption has shot up in recent years, making the prediction of its demand a current challenge to ensure an efficient and responsible use. Artificial intelligence techniques have proven to be potential tools in handling tedious tasks and making sense of large-scale data to make be...

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Bibliographic Details
Published in:Applied sciences 2021-09, Vol.11 (18), p.8635
Main Authors: Cáceres, Laura, Merino, Jose Ignacio, Díaz-Díaz, Norberto
Format: Article
Language:English
Subjects:
Accuracy
Artificial intelligence
Big Data
big data architecture
Calendars
Carbon
Computer applications
Decision trees
Demand
Economic forecasting
Electricity
Energy consumption
Energy demand
energy demand forecasting
Energy management
Households
Machine learning
Predictions
random forest
Regression analysis
Residential energy
Socioeconomic factors
Variables
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Summary:Society’s energy consumption has shot up in recent years, making the prediction of its demand a current challenge to ensure an efficient and responsible use. Artificial intelligence techniques have proven to be potential tools in handling tedious tasks and making sense of large-scale data to make better business decisions in different areas of knowledge. In this article, the use of random forests algorithms in a Big Data environment is proposed for household energy demand forecasting. The predictions are based on the use of information from different sources, confirming a fundamental role of socioeconomic data in consumer’s behaviours. On the other hand, the use of Big Data architectures is proposed to perform horizontal and vertical scaling of the solution to be used in real environments. Finally, a tool for high-resolution predictions with great efficiency is introduced, which enables energy management in a very accurate way.
ISSN:2076-3417
2076-3417
DOI:10.3390/app11188635