Abstract
The increasing penetration of non-programmable renewable energy sources (RES) is enforcing the need for accurate power production forecasts. In the category of hydroelectric plants, Run of the River (RoR) plants belong to the class of non-programmable RES. Data-driven models are nowadays the most widely adopted methodologies in hydropower forecast. Among all, the Artificial Neural Network (ANN) proved to be highly successful in production forecast. Widely adopted and equally important for hydropower generation forecast is the HYdrological Predictions for the Environment (HYPE), a semi-distributed hydrological Rainfall–Runoff model. A novel hybrid method, providing HYPE sub-basins flow computation as input to an ANN, is here introduced and tested both with and without the adoption of a decomposition approach. In the former case, two ANNs are trained to forecast the trend and the residual of the production, respectively, to be then summed up to the previously extracted seasonality component and get the power forecast. These results have been compared to those obtained from the adoption of a ANN with rainfalls in input, again with and without decomposition approach. The methods have been assessed by forecasting the Run-of-the-River hydroelectric power plant energy for the year 2017. Besides, the forecasts of 15 power plants output have been fairly compared in order to identify the most accurate forecasting technique. The here proposed hybrid method (HYPE and ANN) has shown to be the most accurate in all the considered study cases.
Highlights
In a context of increasing penetration of renewable energy plants, accurate and reliable energy forecasts of wind/solar/hydro power and electric loads are required by diverse types of end users, to be implemented for different time horizons, depending on the specific application, to significantly improve their profitability [1]
From the analysis conducted on correlation factors, it has been decided to provide as input to the neural network just the sub-basins highly correlated with the chosen plant (ρ > 0.5), in order to delete those basins scarcely correlated to the plant production
The hyperparameter optimization conducted allows to identify the optimal neural network configuration in order to carry out the forecast
Summary
In a context of increasing penetration of renewable energy plants, accurate and reliable energy forecasts of wind/solar/hydro power and electric loads are required by diverse types of end users (e.g., utilities, TSOs, energy traders, producers), to be implemented for different time horizons, depending on the specific application, to significantly improve their profitability [1]. Hydroelectric plants can be divided in three main categories: Storage Hydro Plants (SHPs), Pumped Storage Plants (PSPs), and Run-of-the-River Plants (RoR). SHPs store water in a reservoir, typically by means of a dam, and water can be released when is needed to generate power. PSPs exploit a lower and an upper reservoir to pump or release water when it is more economically convenient; it is designed to balance peak loads and it is a net consumer of energy [3]. Forecasting activity in power generation aims to predict non-programmable energy sources; non-programmable energy production plants comprises those power plants whose generation is affected by the variability of natural resources throughout hours of the day and seasons: hydroelectric production forecast concerns RoR hydropower and not storage or pumped-storage plants
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
