Abstract
The solar photovoltaic (PV) power forecast is crucial for steady grid operation, scheduling, and grid electricity management. In this work, numerous time series forecast methodologies, including the statistical and artificial intelligence-based methods, are studied and compared fastidiously to forecast PV electricity. Moreover, the impact of different environmental conditions for all of the algorithms is investigated. Hourly solar PV power forecasting is done to confirm the effectiveness of various models. Data used in this paper is of one entire year and is acquired from a 100 MW solar power plant, namely, Quaid-e-Azam Solar Park, Bahawalpur, Pakistan. This paper suggests recurrent neural networks (RNNs) as the best-performing forecasting model for PV power output. Furthermore, the bidirectional long-short-term memory RNN framework delivered high accuracy results in all weather conditions, especially under cloudy weather conditions where root mean square error (RMSE) was found lowest 0.0025, R square stands at 0.99, and coefficient of variation of root mean square error (RMSE) Cv was observed 0.0095%.
Highlights
Electricity is a fundamental instrument to continue socioeconomic activities
The hourly PV electricity output forecast is vital for operation, maintenance, and overcoming the demanding situations faced by the grid-linked PV plants
A simple statistical model for time series forecasting of hourly solar PV electricity, seasonal autoregressive integrated moving average (SARIMA), and the performances of long-short-term memory (LSTM) and bidirectional long-short-term memory (Bi-LSTM) recurrent neural networks have been examined in this paper
Summary
Electricity is a fundamental instrument to continue socioeconomic activities. Pakistan has been facing an electricity shortage for many years due to heavy reliance on expensive imported fuel, suboptimal transmission and distribution systems, and poor revenue collection [1]. In reference [23], Bi-LSTM for accurate forecasting of solar irradiance hourly and daily was proposed in the study based on two different sites. In reference [24], a study of single-layer and multilayer LSTM models was conducted for the accurate forecast of PV power generation. (i) A deep learning Bi-LSTM is proposed as an accurate power forecasting model for grid-connected PV systems in the study (ii) Evaluation and comparison of various forecasting models, including statistical and neural network techniques, for time series forecasting of largescale PV systems (iii) For accuracy concerns, the study examined over multilayers of LSTM (iv) The paper includes the time frames for which the forecasting models under consideration are effective
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