review · IEEE Access
Photovoltaic power generation is inherently variable due to weather dependencies, making precise forecasting vital for balancing and controlling integrated energy systems. This review evaluates five advanced artificial neural network architectures used for solar power prediction: multilayer perceptron, recurrent neural networks, long short-term memory, gated recurrent units, and convolutional neural networks. The study examines internal model mechanisms alongside key influences on prediction accuracy, including meteorological parameters, forecasting horizons, and assessment metrics. Performance assessments show that bidirectional gated recurrent units and bidirectional long short-term memory networks achieve superior forecasting accuracy in both standalone and hybrid configurations. Additionally, the integration of upgraded metaheuristic algorithms substantially enhances predictive capability across various network structures. The review also identifies practical constraints and technical challenges that currently hinder real-world deployment.
Renewable energy grids require dependable power supply estimates to balance electricity generation with demand. By identifying the most accurate neural network structures for solar forecasting, this work provides energy system operators with clearer guidance on selecting predictive tools to handle weather-related power fluctuations, ultimately supporting more stable electricity grids.
This research informs software developers and energy system operators seeking to deploy predictive analytics tools for solar grid management. While focused on algorithm evaluation, the findings directly assist in selecting network designs for power forecast applications. Given the analytical nature of the review and noted practical implementation constraints, these models appear to be at an applied research stage requiring operational validation before commercial deployment.
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Solar energy is largely dependent on weather conditions, resulting in unpredictable, fluctuating, and unstable photovoltaic (PV) power outputs. Thus, accurate PV power forecasts are increasingly crucial for managing and controlling integrated energy systems. Over the years, advanced artificial neural network (ANN) models have been proposed to increase the accuracy of PV power forecasts for various geographical regions. Hence, this paper provides a state-of-the-art review of the five most popular and advanced ANN models for PV power forecasting. These include multilayer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), gated recurrent unit (GRU), and convolutional neural network (CNN). First, the internal structure and operation of these models are studied. It is then followed by a brief discussion of the main factors affecting their forecasting accuracy, including forecasting horizons, meteorological conditions, and evaluation metrics. Next, an in-depth and separate analysis of standalone and hybrid models is provided. It has been determined that bidirectional GRU and LSTM offer greater forecasting accuracy, whether used as a standalone model or in a hybrid configuration. Furthermore, hybrid and upgraded metaheuristic algorithms have demonstrated exceptional performance when applied to standalone and hybrid ANN models. Finally, this study discusses various limitations and shortcomings that may influence the practical implementation of PV power forecasting.
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DOI: 10.1109/access.2024.3420693
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