Eco-Economic Predictions: Applying QPSO-BiLSTM and Attention Mechanisms for Accurate Renewable Energy Forecasting

Abstract

In the pursuit of sustainable development and climate change mitigation, achieving carbon neutrality is a critical goal. This requires balancing energy production with environmental protection, particularly as nations strive to reduce carbon emissions while promoting economic growth. In this context, the accuracy of time-series forecasting related to energy becomes increasingly significant. However, the path to carbon neutrality is fraught with challenges, including volatile energy markets, data integrity concerns, and the complexity of simulating economic indicators alongside emission data.To address these challenges, this study introduces a novel forecasting approach that integrates Quantum Particle Swarm Optimization (QPSO), Bidirectional Long Short-Term Memory networks (BiLSTM), and an attention mechanism. Our method enhances predictive analysis capabilities for energy consumption, production, and economic impacts, demonstrating its substantial value in the field of energy economics. Following extensive training and validation, our model significantly outperforms existing models in time-series forecasting, achieving an accuracy of 97.39% on the National Renewable Energy Laboratory (NREL) dataset, which captures renewable energy patterns, and 97.58% on the Energy Information Administration (EIA) dataset, representing broader energy economic trends. Furthermore, it attains 95.61% accuracy on the European Environment Agency (EEA) dataset and 97.33% accuracy on the Global Carbon Project (GCP) dataset, both of which are critical for environmental and economic planning. The combination of QPSO, BiLSTM, and the attention mechanism enables the model to adapt to the dynamic nature of energy markets and economic indicators, providing a detailed understanding of carbon emission trajectories. The reliability and high accuracy of our model offer valuable decision support to policymakers and stakeholders in the energy sector, facilitating the formulation of carbon neutrality strategies that are both economically viable and environmentally sustainable. Our research results offer compelling evidence for the adoption of advanced analytical techniques in energy economics, aiming to enhance carbon neutrality policy-making and ultimately contribute to a more sustainable future.