Abstract
A reliable and well-understood spot electricity pricing model is desirable for minimizing risks and maximizing profits for the power trading business. Understanding the characteristics of spot market electricity prices will enhance the business confidence of the power market participants. In the Indian power market, however, spot electricity prices are highly volatile with the presence of seasonality and spikes. Such price volatility causes difficulty in predicting future energy prices. In this work, an attempt is made for analysis of day-ahead market prices in Indian Power Exchange (IEX) after visualizing its characteristics at all different time frames (i.e., yearly, monthly, weekly, daily, and hourly). The work also includes application of classical as well as neural network techniques for modeling and forecasting of the same price data to compare forecasting performances. While price data visualization using different statistical tools gives important information on price variations, modeling and forecasting price datasets show higher accuracy with neural network techniques when compared with autoregressive models. Further, neural network techniques with multivariate inputs yield better forecasting edges in comparison with the univariate inputs. The findings of the work can assist sellers/buyers to anticipate price volatility with substantial accuracy of spot electricity price forecasting in the wholesale electricity market of India.
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Saha, M., Pal, N. (2022). Analysis, Modeling, and Forecasting of Day-Ahead Market Prices in Indian Power Exchange. In: Sharma, H., Shrivastava, V., Kumari Bharti, K., Wang, L. (eds) Communication and Intelligent Systems . Lecture Notes in Networks and Systems, vol 461. Springer, Singapore. https://doi.org/10.1007/978-981-19-2130-8_77
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