A Thematic Review of AI-Based Energy Forecasting Approaches: Accuracy, Uncertainty, and Economic Value

Kunshraj Singhania

Abstract: Power grids everywhere now rely more heavily on renewables, turning forecasts once used just for planning into daily necessities. Not like coal or gas plants, wind and sunlight shift constantly, shaped by weather patterns and daylight changes. Because output jumps around unpredictably, keeping supply steady demands extra reserves - costs climb along with uncertainty. To handle swings better, utilities lean increasingly on smart computer methods: neural networks, ensemble tricks, statistical hybrids, pattern-spotting algorithms - all trained to guess sun strength, wind speed, how much people will use, even price shifts. Yet here?s what studies rarely stress - the gap between lab results boasting higher scores and real-world reports talking about messy databases, unclear ownership rules, shaky profit numbers when firms actually roll out these tools. Looking at studies from 2020 to 2025, this review brings together academic and non-academic work exploring if AI-powered energy forecasts beat older statistical methods in cost terms. Focus lands first on predicting renewable output, though insights about demand and pricing pop up too along the way. Instead of old-school stats, many now turn to machine learning - sometimes shallow, sometimes stacked deep. Data cleanliness matters more than clever code, often deciding success before any model runs. Picking between simpler algorithms and complex neural nets involves balancing effort, transparency, and results case by case. Guessing ranges beats single numbers when dealing with sun and wind, so measuring doubt has become standard practice. Better predictions only count if they save money or cut waste, which ties progress back to real-world gains eventually. Nowhere is it guaranteed that tighter error scores mean better real-world results. Even when machines beat older techniques on measures like Mean Absolute Error (Benti et al., 2023; Abisoye, 2024) - how far off predictions are on average - the edge can vanish in practice. Root Mean Square Error, which stresses bigger mistakes more, often favors AI, yet this does not automatically lead to savings or efficiency. Mean Absolute Percentage Error shows percent-level gaps, a common yardstick, but high marks here mislead if deployment costs rise too much. What plays out in spreadsheets may fail under live conditions. Gains shrink when forecasts stretch further ahead in time. Systems able to adjust quickly benefit more from precise inputs. Rules set by regulators shape how useful any prediction method becomes. Heavy computing needs eat into advantages unless hardware keeps pace. Older grids with weak data networks gain little, even with advanced tools running behind the scenes. Looking at India offers clues because of how fast it adds renewables (IEA, 2021; NREL, n.d.) alongside rules tying forecast errors to money consequences. Though machines predict better, they save cash just when systems align a certain way. Coming studies need to weigh costs in comparisons, speed up number crunching, also blend uncertainty estimates into daily choices more tightly.

Keywords: Artificial Intelligence (AI), Energy Forecasting, Renewable Energy Generation, Machine Learning, Deep Learning, Probabilistic Forecasting, Smart Grids

How to Cite?: Kunshraj Singhania, "A Thematic Review of AI-Based Energy Forecasting Approaches: Accuracy, Uncertainty, and Economic Value", Volume 15 Issue 8, August 2026, International Journal of Science and Research (IJSR), Pages: 413-424, https://www.ijsr.net/getabstract.php?paperid=SR26723144942, DOI: https://dx.doi.org/10.21275/SR26723144942

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