Forecasting Electricity Prices for the Feasibility of Renewable Energy Plants

Feasibility studies in assessing the viability of renewable energy investments involve conducting economic analyses to evaluate crucial performance criteria such as return on investment and payback period. In these analyses, it is essential to calculate the profitability derived from the electricity generated and sold, considering the investment and operational costs. At this point, electricity prices emerge as a determining parameter. However, accurately predicting electricity prices is challenging due to the need to account for production costs, as well as regulations and policies within the framework of the free market mechanism. Moreover, compared to other traded commodities, electricity prices are further complicated by electricity’s inability to store, the necessity of instantaneous balancing of production and consumption, and the high seasonality of demand for domestic, industrial and commercial electricity. On the other hand, precise forecasting of future electricity prices is a critical factor that enhances the accuracy of economic analyses. This study employs the Prophet Algorithm, which utilizes time series analysis and considers historical electricity prices to make periodic predictions of future electricity prices. The Prophet algorithm is specifically designed to capture the evidence of deterministic trend and seasonality, and at the same time the effects of an econometric shock, providing reliable results in forecasting electricity prices. Our study has examined and compared the forecasting performance of Python’s Prophet Algorithm and Excel Estimator. Although the Excel Estimator did not achieve the same level of precision, it produced results within an acceptable range.