Short-term electric load forecasting in microgrids: Issues and challenges

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Title

Short-term electric load forecasting in microgrids: Issues and challenges

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Description

This paper compares performance of three well-known short-term load forecasting (STLF) methodologies in microgrid applications. The chosen methods include: I) seasonal auto-regressive integrated moving average with exogenous variables, ii) neural networks, and iii) wavelet neural networks. These methods utilise combinations of historical load data and metrological variables to predict the load of individual customers in a microgrid over the next day. This is essential for scheduling, manageme...

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Date created

27 Apr, 2021

Creator

Hesamoddin Marzooghi / Kianoush Emami / Peter Wolfs / B Holcombe

URL

https://figshare.com/articles/conference_contribution/Short-term_electric_load_forecasting_in_mic...

Related subjects

Decision feedback equalizers / Light fidelity / Uplink / Optical transmitters / Peak to average power ratio / Feedforward systems / Receivers / Artificial intelligence / classical load forecasting approaches / microgrids / short-term load forecasting / Power and Energy Systems Engineering (excl. Renewable Power) / Electrical engineering not elsewhere classified

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