A Novel Approach Based Deep RNN Using Hybrid NARX-LSTM Model For Solar Power Forecasting

2019/10/21 by Mohamed Massaoudi, Massaoudi, Mohamed, Inès Chihi +9
Computer Science · Energy · Engineering · #Energy Load and Power Forecasting #FOS: Electrical engineering #Photovoltaic System Optimization Techniques #Signal Processing (eess.SP) #Solar Radiation and Photovoltaics #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1910.10064

openalex publication_date 2019/10/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The high variability of weather parameters is making photovoltaic energy generation intermittent and narrowly controllable. Threatened by the sudden discontinuity between the load and the grid, energy management for smart grid systems highly require an accurate PV power forecasting model. In this regard, Nonlinear autoregressive exogenous (NARX) is one of the few potential models that handle time series analysis for long-horizon prediction. This later is efficient and high-performing. However, this model often suffers from the vanishing gradient problem which limits its performances. Thus, this paper discus NARX algorithm for long-range dependencies. However, despite its capabilities, it has been detected that this model has some issues coming especially from the vanishing gradient. For the aim of covering these weaknesses, this study suggests a hybrid technique combining long short-term memory (LSTM) with NARX networks under the umbrella of Evolution of recurrent systems with optimal linear output (EVOLINO). For the sake of illustration, this new approach is applied to PV power forecasting for one year in Australia. The proposed model enhances accuracy. This made the proposed algorithm outperform various benchmarked models.

Citations

• On the difficulty of training Recurrent Neural Networks
• Long Short-Term Memory
• Deep Learning in Neural Networks: An Overview
• Sequence to Sequence Learning with Neural Networks

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