首页|An intuitive unified hybrid approach for medium to long-term forecasting of different renewable energy sources
An intuitive unified hybrid approach for medium to long-term forecasting of different renewable energy sources
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NSTL
Forecasting of stochastic renewable energy resources is quintessential for effective planning, operationsand management of the power systems. Existing literature contains ample studies on very short andshort-term forecasting of renewables. But it’s still challenging to obtain high accuracy for medium-termand long-term forecasting. Therefore, an intuitive unified hybrid approach is proposed in this paper formedium to long-term forecasting of power generation from different weather-dependent renewablessingle-handedly by utilising their inherited periodic seasonal patterns iteratively year-on-year basis. Bi-LSTM (Bidirectional Long Short Term Memory) and ARIMA (Auto Regressive Integrated Moving Average)are utilised to construct the proposed approach with the aid of STL (Seasonal-Trend decomposition usingLoess) decomposition and data pre-processing. The performance of proposed approach (STL-ARIMABiLSTM)is validated using seven recent datasets of wind, solar and hydro power. It yields accurate forecastsfor a week-ahead to a year-ahead forecasting horizons. MAE (Mean Absolute Error) lying in range from 6.3%to 6.6%, 5.6% to 6.7% and 4.72% for a year-ahead forecast of wind, hydro and solar power, respectively, isobtained. It is established that these long-term forecast errors are even less than the short-term forecasterrors of some of the existing studies demonstrating novelty and practicality of the proposed approach.
Renewable power generation forecastinghybrid modelARIMABi-LSTMtime-series decompositionunivariate time series analysis
Priyanka Malhan、Monika Mittal
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National Institute of Technology, Kurukshetra, India
NETL
NSTL
