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To enhance the stability of photovoltaic power grid integration and improve power prediction accuracy, a photovoltaic power prediction method based on an improved snow ablation optimization algorithm (Good Point and Vibration Snow Ablation Optimizer, GVSAO) and Bi-directional Long Short-Term Memory (Bi-LSTM) network is proposed. Weather data is divided into three typical categories using K-means clustering, and data normalization is performed using the minmax method. The key structural parameters of Bi-LSTM, such as the feature dimension at each time step and the number of hidden units in each LSTM layer, are optimized based on the Good Point and Vibration strategy. A prediction model is constructed based on GVSAO-Bi-LSTM, and typical test functions are selected to analyze and evaluate the improved model. The research results show that the average absolute percentage error of the GVSAO-Bi-LSTM prediction model under sunny, cloudy, and rainy weather conditions are 4.75%, 5.41%, and 14.37%, respectively. Compared with other methods, the prediction results of this model are more accurate, verifying its effectiveness. [ABSTRACT FROM AUTHOR]

At present, establishing a multidimensional characteristic model of a boiler combustion system plays an important role in realizing its dynamic optimization and real-time control, so as to achieve the purpose of reducing environmental pollution and saving coal resources. However, the complexity of the boiler combustion process makes it difficult to model it using traditional mathematical methods. In this paper, a kind of hyper-parameter self-optimized broad learning system by a sparrow search algorithm is proposed to model the NOx, SO2 emissions concentration and thermal efficiency of a circulation fluidized bed boiler (CFBB). A broad learning system (BLS) is a novel neural network algorithm, which shows good performance in multidimensional feature learning. However, the BLS has several hyper-parameters to be set in a wide range, so that the optimal combination between hyper-parameters is difficult to determine. This paper uses a sparrow search algorithm (SSA) to select the optimal hyper-parameters combination of the broad learning system, namely as SSA-BLS. To verify the effectiveness of SSA-BLS, ten benchmark regression datasets are applied. Experimental results show that SSA-BLS obtains good regression accuracy and model stability. Additionally, the proposed SSA-BLS is applied to model the combustion process parameters of a 330 MW circulating fluidized bed boiler. Experimental results reveal that SSA-BLS can establish the accurate prediction models for thermal efficiency, NOx emission concentration and SO2 emission concentration, separately. Altogether, SSA-BLS is an effective modelling method. [ABSTRACT FROM AUTHOR]

Deep neural networks are rapidly gaining popularity. However, their application requires setting multiple hyper-parameters, and the performance relies strongly on this choice. We address this issue and propose a robust ex-ante hyper-parameter selection procedure for the day-ahead electricity price forecasting that, when used jointly with a tested forecast averaging scheme, yields high performance throughout three-year long out-of-sample test periods in two distinct markets. Being based on a grid search with models evaluated on long samples, the methodology mitigates the noise induced by local optimization. Forecast averaging across calibration window lengths and hyper-parameter sets allows the proposed methodology to outperform a parameter-rich least absolute shrinkage and selection operator (LASSO)-estimated model and a deep neural network (DNN) with non-optimized hyper-parameters in terms of the mean absolute forecast error. [ABSTRACT FROM AUTHOR]