ZAPP reduces percentage errors in near-zero solar power forecasts

A zero-aware machine-learning framework called ZAPP substantially reduced percentage errors in photovoltaic power forecasting when measured across two datasets, according to a study evaluating the method against several base models. For the tested hybrid Ridge–XGBoost configuration, symmetric mean absolute percentage error, or sMAPE, fell from 60.57% to 11.07% on Dataset 1 and from 63.30% to 11.92% on Dataset 2. The authors report that the correction primarily reduced percentage errors without significantly weakening overall regression performance.

Solar power output can be difficult to predict accurately when a photovoltaic system is producing little or no electricity because even a small absolute error can translate into a large percentage error. ZAPP addresses this evaluation problem by placing two refinement stages after a base regression model. The first, called ZeroGate, predicts whether the output should be zero. The second is a low-power MAPE reducer that adjusts non-zero predictions close to the zero level. This structure allows the framework to focus additional modeling on the region of the output range where percentage-based error measures are especially sensitive.

The researchers evaluated ZAPP with six base-model configurations: Persistence, Ridge, XGBoost, long short-term memory, a Transformer and a hybrid Ridge–XGBoost model. The two photovoltaic datasets were divided into training, validation and test sets in chronological order. Under that protocol, test data were excluded from model development, threshold tuning, ensemble weighting and calibration. The reported metrics for the hybrid configuration showed that the framework did not merely improve its headline percentage-error measure. On Dataset 1, its final mean absolute error was 22.37, root mean square error was 60.72 and R² was 0.9637.

Results on Dataset 2 were 52.24 for MAE, 144.60 for RMSE and 0.9559 for R². These values indicate strong overall fit for the evaluated forecasts, while the sMAPE reductions document the principal reported effect of the zero-aware correction. The findings are limited to the two datasets, the selected base models and the study’s chronological evaluation protocol; they do not establish that ZAPP will deliver identical gains at other locations, under different weather regimes or with every forecasting system. Nevertheless, the experiments provide a quantitative demonstration that explicitly handling zero and near-zero photovoltaic outputs can improve percentage-error measures while retaining high scale-dependent accuracy.

Subject of Research: Evaluation of ZAPP for photovoltaic power forecasting near zero output

Article Title: ZAPP: a zero-aware photovoltaic power prediction framework

Article References: Gürhanlı, A., & Esen, V. (2026). ZAPP: a zero-aware photovoltaic power prediction framework. Scientific Reports. https://doi.org/10.1038/s41598-026-75046-6

Image Credits: AI Generated

DOI: 10.1038/s41598-026-75046-6

Keywords: photovoltaic power, solar forecasting, zero-aware modeling, percentage error, sMAPE, Ridge regression, XGBoost, LSTM, Transformer, machine learning, ZAPP, zero-aware