Short term load forecasting based on ARIMA and ANN approaches

نویسندگان

چکیده

Forecasting electricity demand requires accurate and sustainable data acquisition systems which rely on smart grid systems. To predict the expected by grid, many meters are required to collect sufficient data. However, problem is multi-dimensional simple power aggregation techniques may fail capture relational similarities between various types of users. Therefore, forecasting energy plays a key role in planning, setting up, implementing networks for renewable systems, continuously providing consumers. This also element planning requirement storage devices their capacity. Additionally, errors hour-to-hour cause considerable economic consumer losses. paper aims address knowledge gap based machine learning (ML) predicting load using two methods: Auto Regressive Integrated Moving Average (ARIMA) Artificial Neural Network (ANN); compares performance both methods Mean Absolute Percentage Error (MAPE). The study daily real 709 individual households were randomly chosen over an 18-month period Ireland. results reveal that (ANN) offers better than ARIMA non-linear

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ژورنال

عنوان ژورنال: Energy Reports

سال: 2023

ISSN: ['2352-4847']

DOI: https://doi.org/10.1016/j.egyr.2023.01.060