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Forecasting of stochastic renewable energy resources is quintessential for effective planning, operations and management of the power systems. Existing literature contains ample studies on very short and short-term forecasting of renewables. But it’s still challenging to obtain high accuracy for medium-term and long-term forecasting. Therefore, an intuitive unified hybrid approach is proposed in this paper for medium to long-term forecasting of power generation from different weather-dependent renewables single-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 using Loess) decomposition and data pre-processing. The performance of proposed approach (STL-ARIMA-BiLSTM) is validated using seven recent datasets of wind, solar and hydro power. It yields accurate forecasts for 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, is obtained. It is established that these long-term forecast errors are even less than the short-term forecast errors of some of the existing studies demonstrating novelty and practicality of the proposed approach.

An intuitive unified hybrid approach for medium to long-term forecasting of different renewable energy sources