ABSTRACT
Background and objective: Tracking clean electricity generation in developing economies is highly challenging owing to the influence of turbulent external factors. Clean electricity is a significant enabler of striving toward environmental sustainability. In this research, we aim to model hydro, nuclear, and renewable electricity generation in India through applied predictive modeling. We also strive to uncover the influence of the critical determinants responsible for clean electricity growth. Methodology: We propose a granular predictive framework comprising ensemble empirical mode decomposition, clustering applications in spatial data based on density, including noise, and atom search optimization-based novel optimization methodology to predict absolute figures of clean energy generation. The framework uses a series of socio-economic factors reflecting household demand and industrial growth in India as explanatory variables. Results: The rigorous scrutiny of the predictive framework specifies hydro electricity generation is relatively more predictable during the time horizon influenced by the COVID-19 pandemic. The deployment of dedicated explainable artificial intelligence (AI) tools suggests an increased adoption of clean electricity in selected industrial sectors in India, which broadly governs the evolutionary pattern. Conclusion: The underlying research is the first of its kind to fathom the daily temporal dynamics of clean electricity generation in the Indian context. Consideration of three distinct clean electricity sources during highly volatile time regimes underscores the contribution of the work. The predictive framework survives a stringent performance check, which justifies the robustness of the same. Demand in different industrial sectors in India profoundly influences the growth toward clean electricity.
ABSTRACT
The outbreak of the COVID-19 pandemic has transpired the global media to gallop with reports and news on the novel Coronavirus. The intensity of the news chatter on various aspects of the pandemic, in conjunction with the sentiment of the same, accounts for the uncertainty of investors linked to financial markets. In this research, Artificial Intelligence (AI) driven frameworks have been propounded to gauge the proliferation of COVID-19 news towards Indian stock markets through the lens of predictive modelling. Two hybrid predictive frameworks, UMAP-LSTM and ISOMAP-GBR, have been constructed to accurately forecast the daily stock prices of 10 Indian companies of different industry verticals using several systematic media chatter indices related to the COVID-19 pandemic alongside several orthodox technical indicators and macroeconomic variables. The outcome of the rigorous predictive exercise rationalizes the utility of monitoring relevant media news worldwide and in India. Additional model interpretation using Explainable AI (XAI) methodologies indicates that a high quantum of overall media hype, media coverage, fake news, etc., leads to bearish market regimes.
