AI-Powered Neural Networks Drive Renewable Energy and Emission Predictions

By Dr Silpaja Chandrasekar, PhDReviewed by Joel ScanlonNov 21 2024

Artificial neural networks reshape how we predict renewable energy performance and reduce emissions, paving the way for a sustainable future.

Review: Analysis of Neural Networks Used by Artificial Intelligence in the Energy Transition with Renewable Energies. Image Credit: Shutterstock AI

In a paper published in the journal Applied Sciences, researchers at the University of Oviedo, Spain, and the University of Bielefeld, Germany, analyzed the global applications of artificial neural networks (ANNs) for energy transition, focusing on solar, wind, and tidal energy, as well as the prediction of greenhouse gas (GHG) emissions. They reviewed 96 studies spanning the most recent research from 2018 to 2022.

The results highlighted multilayer perceptrons (MLPs) as the predominant structure, using purelin, tansig, and logsig activation functions. The analysis was based on architecture, inputs/outputs, regions, activation functions, and training algorithms.

Related Work

Past work highlighted the development of artificial intelligence (AI) models inspired by the human brain, with ANNs emerging as powerful tools for multivariate system modeling. ANNs have evolved significantly since their inception in the 1940s, finding applications in diverse fields, including renewable energy, transportation systems, and environmental impact mitigation. Researchers have classified ANN models by their structure and primary use cases and analyzed their use in energy transition scenarios, emphasizing applications in buildings, transport, and greenhouse gas emissions prediction.

ANN Applications in Renewable Energy

Due to their significant contributions to national energy balances, research into the application of ANNs in renewable energy has focused primarily on wind, solar, and tidal energy. In wind energy, ANNs have shown promise in predicting wind speed and power generation, which is crucial for the efficient operation of wind farms.

Studies have primarily used MLP networks, with simple structures comprising one or two hidden layers. Inputs to these models include factors like historical wind speed, temperature, and humidity, while activation functions such as tansig and logsig are standard in the hidden layers. The models have been widely adopted in countries like Turkey, India, and China, demonstrating high accuracy and efficiency in forecasting.

For solar energy, ANNs offer robust and efficient modeling alternatives requiring minimal knowledge of internal system parameters. They have been used to predict solar irradiance, optimize the performance of photovoltaic systems, and improve the efficiency of solar collectors, thus reducing costs and experimental efforts. Again, MLP networks dominate, with single hidden layers being the most common. Inputs often include latitude, relative humidity, and solar radiation data.

Lastly, in tidal energy, ANNs have become vital for predicting wave height and period, with studies concentrated in India, Canada, and the United States. The ANN models typically utilize historical wave data, wind speed, and direction, with predictions ranging from one to 24 hours in advance, contributing to advancements in ocean and coastal engineering.

GHG Prediction Methods

ANNs, especially MLPs, have been widely used for greenhouse gas (GHG) prediction. These models require less complex input data than traditional methods like the GAINS model. Research from 1996 to 2021 has employed chiefly simple ANN structures with one hidden layer, utilizing gases such as CO2 and CH4 as outputs and macroeconomic or meteorological variables as inputs. Activation functions like tansig and logsig are standard in the hidden layers, while purelin and sigmoid are used for outputs, demonstrating practical and scalable GHG estimation.

ANN Research Trends

The analysis of research trends in applying ANNs for renewable energy and GHG prediction highlighted the essential role ANNs play in deep learning within AI. Previous studies emphasized the importance of ANNs in renewable energy applications. Additionally, researchers highlighted ethical considerations, including data privacy, algorithmic transparency, and environmental impacts related to training large ANN models.

Another method demonstrated the effectiveness of ANNs in modeling energy resources with errors within acceptable tolerances. A review of the literature from 1996 to 2021 revealed a consistent interest in ANNs, with wind power and GHG prediction research being the most prominent, followed by wave and solar energy studies.

The geographical analysis showed widespread interest across 30 countries, with the United States and India focusing particularly on wave and GHG prediction. Turkey stood out for solar energy research, leading in the number of publications. Methodologically, most studies used MLP networks, especially for GHG and solar energy predictions. The preference for MLP indicates its reliability and adaptability in diverse ANN research, with variations depending on the application type.

The review also highlighted tailored activation function trends, with purelin for the output layer and tansig for hidden layers widely used in wind speed prediction. Wave prediction favored purelin and sigmoid for output layers, while solar energy studies often employed purelin, tansig, and logsig. These choices reflect application-specific requirements, underscoring ANN flexibility in addressing diverse renewable energy challenges.

Conclusion

In summary, ANNs proved reliable for predicting energy resources and pollutant emissions, with significant research published in prestigious journals. Most studies focused on renewable energy and GHG emissions, employing MLP architectures with common training algorithms like backpropagation.

The reviewed work emphasized the need for broader applications across all energy sectors, aiming for competitive and sustainable systems. Additionally, future research could explore ANN use in energy-recoverable biomass, sustainable mobility, and energy optimization, enhancing energy efficiency and reducing emissions.