Data-Driven Load Forecasting Using Machine Learning and Meteorological Data
نویسندگان
چکیده
Electrical load forecasting is very crucial for electrical power systems’ planning and operation. Both buildings’ demand meteorological datasets may contain hidden patterns that are required to be investigated studied show their potential impact on forecasting. The data analyzed in this study through different mining techniques aiming predict the of a factory located Riyadh, Saudi Arabia. used recorded hourly between 2016 2017. These provided by King Abdullah City Atomic Renewable Energy Electricity Company at site Riyadh. After applying pre-processing prepare data, machine learning algorithms, namely Artificial Neural Network Support Vector Regression (SVR), applied compared load. In addition, sake selecting optimal set features, 13 combinations features study. outcomes emphasize as more add complexity process. Finally, SVR algorithm with six provides most accurate prediction values
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ژورنال
عنوان ژورنال: Computer systems science and engineering
سال: 2023
ISSN: ['0267-6192']
DOI: https://doi.org/10.32604/csse.2023.024633