Computationally expedient Photovoltaic power Forecasting: A LSTM ensemble method augmented with adaptive weighting and data segmentation technique. (15th April 2022) - Ahmed, Razin; Sreeram, Victor; Togneri, Roberto; Datta, Amitava; Arif, Muammer Din
Highlights: Comparative analysis of data scaling parameters, input feature selection with univariate/multivariate preferences. Evaluation of the Long Short Term Memory model using Data Segmentation structures, varied time-horizons, and case studies. An ensemble boosting approach with an adjustable weighting mechanism is developed to optimize forecast accuracy. Runtime assessment and viability of the proposed approach. Abstract: Photovoltaics (PVs) hold the promise of sustainable electricity production. However, PV output is significantly influenced by variations in terrestrial solar radiation and other weather factors, causing problems in unit commitment, economic power dispatch and reliable electricity distribution from grid-connected, hybrid and off-grid PV plants. Thus, accurate and consistent PV output prediction is crucial for utilities companies and researchers. Current initiatives enlist a plethora of forecasting techniques, including: statistical models, physical models, artificial neural networks and deep learning algorithms. Nevertheless, most of these approaches suffer from computational costs, complex models and uncertainties. Hence, this research employed an ensemble-based Long Short-Term Memory (LSTM) algorithm comprising 10 component LSTM models. The method compares the effects of different data segmentations (three-months to one-day) and is based on varying time-horizons (14-days to 5-mins) in order to compare the effects of seasonal and periodic variations on time-series data and PV output forecast. The comparison is then used to minimise uncertainty by implementing grid search technique. Subsequently, the model's performance was evaluated using MAPE analysis for two cases involving erratic weather conditions and PV output in two specific days of the year 2020. From the evaluation, the best prediction was found to be for the two-week dataset with MAPE of 6.02. This approach combined with online data acquisition from the Yulara Solar System power plant in central Australia leads to a more practical, computationally economical and robust PV power generation forecasting technique.