Abstract

The rising integration of variable renewable energy sources (RES), like solar and wind power, introduces considerable uncertainty in grid operations and energy management. Effective forecasting models are essential for grid operators to anticipate the net load - the difference between consumer electrical demand and renewable power generation. This paper proposes a deep learning (DL) model based on long short-term memory (LSTM) networks for net load forecasting in renewable-based microgrids, considering both solar and wind power. The model’s architecture is detailed, and its performance is evaluated using a residential microgrid test case based on a typical meteorological year (TMY) dataset. The results demonstrate the effectiveness of the proposed LSTM-based DL model in predicting the net load, showcasing its potential for enhancing energy management in renewable-based microgrids.

Index Terms

Long Short-Term Memory, Microgrid, Net Load Forecasting, Recurrent Neural Network, Renewable Energy.

Cite this paper:

Jesus Silva-Rodriguez, Elias Raffoul, and Xingpeng Li, “LSTM-Based Net Load Forecasting for Wind and Solar Power-Equipped Microgrids”, North American Power Symposium, El Paso, TX, USA, Oct. 2024.

(Jesus Silva-Rodriguez and Elias Raffoul contributed equally to this work.)