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Photovoltaic (PV) modules convert renewable and sustainable solar energy into electricity. However, the uncertainty of PV power production brings challenges for the grid operation. To facilitate the management and scheduling of PV power plants, forecasting is an essential technique. In this paper, a robust multilayer perception (MLP) neural network was developed for day-ahead forecasting of hourly PV power. A generic MLP is usually trained by minimizing the mean squared loss. The mean squared error is sensitive to a few particularly large errors that can lead to a poor estimator. To tackle the problem, the pseudo-Huber loss function, which combines the best properties of squared loss and absolute loss, was adopted in this paper. The effectiveness and efficiency of the proposed method was verified by benchmarking against a generic MLP network with real PV data. Numerical experiments illustrated that the proposed method performed better than the generic MLP network in terms of root mean squared error (RMSE) and mean absolute error (MAE).

Parameter estimation of Lorenz chaotic system using a novel hybrid Jaya-Powell algorithm is proposed in this paper. Since the nonlinear dynamic system is complex with multi-dimension parameters, estimating parameters of the system can be considered as a muti-objective optimization task. The proposed Jaya-Powell algorithm combines the Jaya and Powell algorithm to search for the relatively global optimum and local optimum respectively, which offers a more accurate and effective estimation. The searching strategy of the proposed algorithm facilitates the balance of the exploration and exploitation in the optimization procedure. Due to no algorithm-specific parameters are required in the Jaya and Powell algorithm, the proposed Jaya-Powell can avoid deliberate fine-tuning of corresponding parameters. To validate the accuracy and robustness of the proposed algorithm in parameter estimation, the simulation of Lorenz chaotic system and comparative experiments are conducted. Seven algorithms, including Jaya algorithm, Powell algorithm, Teaching-learning-based optimization (TLBO) algorithm, particle swarm optimization (PSO), genetic algorithm (GA), covariance matrix adaptation evolution strategy (CMA-ES), and cluster-chaotic-optimization algorithm (CCO), are considered as benchmarking algorithms in the comparison. The proposed hybrid Jaya-Powell algorithm outperforms seven benchmarking algorithms with the more accurate estimation and the relatively faster convergence. Based on the embedded system Raspberry pi 3, the proposed algorithm achieves the similar performance by comparing with the experiments conducted on the computer. The successful implementation via Raspberry pi 3 facilitates the application of the proposed algorithm in edge computing.

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