Addressing utility demand prediction challenges: Integrating solar and wind energy for nighttime reliability
Data Engineering Lead, Portland General Electric, Raleigh, NC, USA.
Review
International Journal of Scholarly Research in Engineering and Technology, 2025, 06(01), 001-008.
Article DOI: 10.56781/ijsret..2025.6.1.0028
Publication history:
Received on 21 June 2025; revised on 27 July 2025; accepted on 30 July 2025
Abstract:
The transition toward wind and solar energy has created major difficulties for utility companies to accurately forecast and fulfill nighttime electricity requirements. Solar power generates substantial amounts during daytime but stops producing electricity when night falls thus creating essential power shortages. Wind energy remains available at night, but its output depends on both location and natural weather patterns. The research examines how utility companies handle supply-demand equilibrium through their operational and predictive challenges when dealing with renewable energy limitations. The paper examines modern demand prediction techniques which combine machine learning algorithms with weather and load and market data integration in forecasting models. Utility providers can enhance their demand forecasting precision and maintain grid stability through these technologies and strategies which help manage nighttime renewable energy uncertainty.
Keywords:
Renewable energy integration; Utility demand prediction; Nighttime electricity supply; Solar energy intermittency; Wind energy variability; Machine learning forecasting; Hybrid energy models; Grid reliability; Load forecasting
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