Adaptive Neuro‐Fuzzy Inference System integrated with solar zenith angle for forecasting sub‐tropical Photosynthetically Active Radiation

Abstract

Advocacy for climate mitigation aims to minimize the use of fossil fuel and to support clean energy adaptation. While alternative energies (e.g., biofuels) extracted from feedstock (e.g., micro‐algae) represent a promising role, their production requires reliably modeled photosynthetically active radiation (PAR). PAR models predict energy parameters (e.g., algal carbon fixation) to aid in decision‐making at PAR sites. Here, we model very short‐term (5‐min scale), sub‐tropical region's PAR with an Adaptive Neuro‐Fuzzy Inference System model with a Centroid‐Mean (ANFIS‐CM) trained with a non‐climate input (i.e., only the solar angle, θZ). Accuracy is benchmarked against genetic programming (GP), M5Tree, Random Forest (RF), and multiple linear regression (MLR). ANFIS‐CM integrates fuzzy and neural network algorithms, whereas GP adopts an evolutionary approach, M5Tree employs binary decision, RF employs a bootstrapped ensemble, and MLR uses statistical tools to link PAR with θZ. To design the ANFIS‐CM model, 5‐min θZ (01–31 December 2012; 0500H–1900H) for sub‐tropical, Toowoomba are utilized to extract predictive features, and the testing accuracy (i.e., differences between measurements and forecasts) is evaluated with correlation (r), root‐mean‐square error (RMSE), mean absolute error (MAE), Willmott (WI), Nash–Sutcliffe (ENS), and Legates & McCabes (ELM) Index. ANFIS‐CM and GP are equivalent for 5‐min forecasts, yielding the lowest RMSE (233.45 and 233.01μ mol m−2s−1) and MAE (186.59 and 186.23 μmol m−2s−1). In contrast, MLR, M5Tree, and RF yields higher RMSE and MAE [(RMSE = 322.25 μmol m−2s−1, MAE = 275.32 μmol m−2s−1), (RMSE = 287.70 μmol m−2s−1, MAE = 234.78 μmol m−2s−1), and (RMSE = 359.91 μmol m−2s−1, MAE = 324.52 μmol m−2s−1)]. Based on normalized error, ANFIS‐CM is considerably superior (MAE = 17.18% versus 19.78%, 34.37%, 26.39%, and 30.60% for GP, MLR, M5Tree, and RF models, respectively). For hourly forecasts, ANFIS‐CM outperforms all other methods (WI = 0.964 vs. 0.942, 0.955, 0.933 & 0.893, and ELM = 0.741 versus 0.701, 0.728, 0.619 & 0.490 for GP, MLR, M5Tree, and RF, respectively). Descriptive errors support the versatile predictive skills of the ANFIS‐CM model and its role in real‐time prediction of the photosynthetic‐active energy to explore biofuel generation from micro‐algae, studying food chains, and supporting agricultural precision.

Forecasting Photosynthetic Photon Flux Density Under Cloud Effects: Novel Predictive Model Using Convolutional Neural Network Integrated With Long Short-term Memory Network

“…Taken together, the present analyses clearly ascertain that at least two of the cloud chromatic properties (i.e., whole sky blue & blue cloud pixel averages associated with measured PPFD) are more strongly correlated with PPFD, compared with the solar zenith angle used in earlier studies. This deduction confirms that the inclusion of cloud cover properties may be a crucial task used to improve earlier models for photosynthetic-active radiation (e.g., [39]).…”

“…It is noteworthy that successive addition of series based on rcross concurs with earlier prediction problems [95] aimed at evaluating potential improvements in CLSTM model. To evaluate the utility of a cloud-free model, a standard approach used in photosynthetic-active radiation [39], solar UV index [88] and global solar models [95]), a CLSTM model designated as M18, with only the SZA, was constructed as a reference model without any inclusion of cloud cover properties.…”

“…In a previous study, the near real-time PPFD prediction model of Deo et al [39] was based on an adaptive neuro-fuzzy inference system to predict PPFD over 5-minute horizons in Queensland (Australia), using time lagged SZA data under cloud-free conditions. Utilising the local solar zenith angle (SZA) as the only input variable, they demonstrated good accuracy in predicting the real-time PPFD with changes in SZA for 5 minute and hourly forecasts.…”

Forecasting standardized precipitation index using data intelligence models: regional investigation of Bangladesh

“…The capacity of the RF has been approved in modeling different phenomena in atmospheric, hydrological and geosciences engineering 58 , environmental management 59 , drought forecasting 60 , rainfall forecasting 61 , solar index estimation 62 and most recently forecasting soil moisture 63 .…”

Deep Learning Neural Networks Trained with MODIS Satellite-Derived Predictors for Long-Term Global Solar Radiation Prediction

“…From a practical point of view, the future planning for an electricity grid certainly requires the prediction of solar radiation a few months ahead of time [29]; therefore, a monthly predictive model is particularly desirable. That model can be useful for agricultural crop growth [30], production of algal-derived biofuels [31], and key decisions made for many applications, where the estimation of long-term solar radiation may be required.…”