LSTM-Based Net Load Forecasting for Wind and Solar Power-Equipped Microgrids

TL;DR

This paper introduces an LSTM-based deep learning model for accurately forecasting net load in renewable-powered microgrids, aiding better energy management amidst the variability of solar and wind energy sources.

Contribution

It presents a novel LSTM-based forecasting model tailored for microgrids with solar and wind power, demonstrating improved prediction accuracy over traditional methods.

Findings

The LSTM model effectively predicts net load in a residential microgrid.

The model outperforms traditional forecasting approaches.

Results show potential for enhanced microgrid energy management.

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.