Hybrid Deep Learning for Wind Power Forecasting: From Grid Stability to Market Equity for Small Generators
Abstract
This paper develops a conceptual framework for wind power forecasting and grid integration that treats engineering innovation and socio-technical analysis as a single integrated design problem rather than as separate concerns addressed in sequence. The framework is motivated by a persistent pattern in which technically sound interventions falter in the field because the social and institutional conditions that govern their durability are neither measured nor designed for. In response, this paper articulates a model in which social variables, centrally forecasting as a quasi-public good whose accessibility determines fair market participation, are rendered explicit and brought into the design problem on the same footing as technical parameters. The contribution is conceptual: this paper sets out the foundations of the framework, specifies its components and the relationships among them, illustrates its reasoning, and derives design and policy implications, with the aim of supporting energy systems that are simultaneously technically efficient, economically accessible, socially responsive, and adaptable to developing and resource-dependent regions
How to Cite This Article
Nenubari Marvin Komi (2021). Hybrid Deep Learning for Wind Power Forecasting: From Grid Stability to Market Equity for Small Generators . International Journal of Multidisciplinary Research and Growth Evaluation (IJMRGE), 2(6), 989-1014. DOI: https://doi.org/10.54660/.IJMRGE.2021.2.6.989-1014