Abstract:

The global transition toward sustainable and decentralized energy systems has spotlighted hybrid renewable energy frameworks (HREFs) as a practical solution for clean and reliable power generation. These systems combine multiple renewable sources—such as solar, wind, biomass, and small hydro—to ensure resilience and environmental sustainability. However, the intermittent nature of renewables necessitates intelligent load management strategies. This paper explores a consumer-centric approach to load management, which integrates smart technologies, demand-side participation, and predictive analytics to align energy supply with real-time consumer demand. The adoption of artificial intelligence (AI), machine learning (ML), and home energy management systems (HEMS) within hybrid frameworks enhances energy efficiency, user comfort, and grid reliability. Additionally, the role of policy and regulatory support in promoting such systems is discussed. The study concludes that consumer-centric load management is not only feasible but imperative for achieving the full potential of decentralized renewable energy systems.

Description:

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