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Extreme Learning Machine Optimized via Differential Evolution for Photovoltaic Output Power Prediction Under Dynamic Weather

Lima Hongou
Volume 5

Abstract

Accurate photovoltaic (PV) output power prediction is essential for reliable renewable energy integration, smart grid operation, energy storage scheduling, and real-time power dispatch. PV power generation is highly sensitive to dynamic weather conditions, including solar irradiance fluctuation, ambient temperature variation, cloud movement, humidity changes, wind speed, atmospheric pressure, and seasonal effects. These factors introduce nonlinear and non-stationary behavior into PV output, making accurate prediction difficult using conventional statistical models and manually tuned machine learning approaches. To address these challenges, this paper proposes an Extreme Learning Machine optimized via Differential Evolution for accurate photovoltaic output power prediction under dynamic weather conditions.

The proposed framework combines the fast learning capability of Extreme Learning Machine (ELM) with the global optimization strength of Differential Evolution (DE). ELM is employed as a single-hidden-layer feed-forward neural network capable of rapid training and effective nonlinear function approximation. However, standard ELM performance can be affected by randomly assigned input weights and hidden-layer biases, which may lead to unstable prediction accuracy and limited generalization. Therefore, Differential Evolution is integrated to optimize critical ELM parameters, including input weights, hidden neuron biases, activation function parameters, and the number of hidden neurons. By guiding the search toward more effective parameter configurations, the DE-optimized ELM improves prediction stability, convergence behavior, and forecasting accuracy compared with conventional ELM.

The proposed model utilizes historical PV output power and meteorological variables such as global horizontal irradiance, direct normal irradiance, diffuse horizontal irradiance, ambient temperature, module temperature, wind speed, humidity, cloud cover, and solar zenith angle. A comprehensive preprocessing pipeline is applied to handle missing measurements, remove abnormal sensor readings, normalize input features, align weather and PV generation records, and construct lagged time-series variables that represent recent weather and power trends. Feature selection and correlation analysis are incorporated to identify the most influential variables affecting PV power output under rapidly changing environmental conditions.

The proposed DE-ELM model can be evaluated using real-world PV plant monitoring data, meteorological station records, and publicly available solar energy datasets. Prediction performance is assessed using standard forecasting metrics, including Mean Absolute Error, Root Mean Square Error, Mean Absolute Percentage Error, normalized RMSE, coefficient of determination, forecasting skill score, and computational training time. Comparative experiments are conducted against persistence models, ARIMA, support vector regression, random forest, XGBoost, standard ELM, multilayer perceptron, LSTM, GRU, and other metaheuristic-optimized learning models. Experimental evaluation is expected to demonstrate that the proposed DE-ELM achieves higher prediction accuracy, faster training speed, and stronger robustness under highly variable weather conditions compared with non-optimized and conventionally tuned models.

For practical smart grid applications, the predicted PV output can be integrated into energy management systems to support battery storage scheduling, grid balancing, demand response, renewable curtailment reduction, and day-ahead or intra-day dispatch planning. In addition, sensitivity analysis is incorporated to evaluate the effect of irradiance, temperature, cloud cover, and wind speed on PV power prediction. These analytical insights can assist grid operators and PV plant managers in understanding the key drivers of prediction uncertainty and improving operational decision-making.

The proposed Differential Evolution-optimized Extreme Learning Machine provides a fast, accurate, and computationally efficient solution for photovoltaic output power prediction under dynamic weather conditions. By combining rapid neural learning with evolutionary optimization, the framework improves forecasting reliability, reduces uncertainty in renewable power generation, and supports more stable integration of solar energy into smart grids and distributed energy systems.

 

Keywords

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