Comparative Analysis of Prediction Accuracy in Clean Energy Forecasting: Implications for Pricing and Profitability in Wind and Biogas Energy Systems
Springer Proceedings in Earth and Environmental Sciences, Springer Nature, ss.14-31, 2026
- Yayın Türü: Kitapta Bölüm / Araştırma Kitabı
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/978-3-032-23955-6_2
- Yayınevi: Springer Nature
- Sayfa Sayıları: ss.14-31
- Anahtar Kelimeler: biogas, electricity pricing, machine learning, prediction cost, wind energy
- Orta Doğu Teknik Üniversitesi Adresli: Evet
Özet
Accurate forecasting of clean energy production is crucial for optimizing energy systems, ensuring grid stability, and promoting sustainable development. However, environmental variability and biological processes introduce significant challenges. Energy harvesting methods, such as wind and biogas (anaerobic digestion), operate on different time horizons, with wind energy being highly sensitive to fluctuating meteorological conditions, while biogas production is influenced by feedstock availability, composition, microbial activity, temperature, and reactor conditions. Seasonal shifts, atmospheric disturbances, and geographical variations further increase uncertainty in real-time yield estimation. However, these factors can be controlled to some extent through process optimization, making biogas production more adaptable compared to wind energy. Wind energy is highly unpredictable due to real-time weather dependence, requiring frequent forecasts and complex capacity planning. In contrast, biogas energy forecasting presents challenges due to its broader time horizon of 25–30 days, yet its more stable daily cycle and controlled processing parameters like feedstock and reactor conditions allow for more consistent predictions. The adoption of these energy sources varies globally, with China leading as the largest wind energy producer, alongside Germany and the United States, which have also made substantial investments in wind power. Germany is the largest producer of biogas, while China and India are expanding their biogas infrastructure for rural electrification and waste management. This study conducts a comparative analysis of wind and biogas energy forecasting, assessing the impact of prediction accuracy on electricity pricing and profitability. Using machine learning techniques, including deep learning and hybrid models, we evaluate forecasting errors and their effects on energy market stability and financial outcomes. Given wind energy’s chaotic nature, we anticipate greater forecasting errors compared to biogas, potentially leading to higher price fluctuations. By integrating pricing models, we analyze the economic implications of prediction accuracy and its role in revenue generation.