Monte Carlo simulation for the prediction of precision of absorbance measurements with a miniature CCD spectrometer

The precision characteristics of the absorbance measurements obtained with a low-cost miniature spectrometer incorporating an array detector were evaluated. Uncertainties in absorbance measurements were due to a combination of non-uniform light intensity and detector response over the wavelength range examined (350-850 nm), in conjunction with the digitization of the intensity indications and the intrinsic noise of the detecting elements. The precision characteristics are presented as contour plots displaying the expected RSD% of absorbances on the absorbance versus wavelength plane. The minimum RSD% for the spectrometer configuration tested was observed within the 0.2-1.5 absorbance units and 500-750 nm wavelength range. Without invoking signal enhancement features of the data-acquisition program (scan average, higher integration times, smoothing based on averaging the signal detected by adjacent pixels), the attainable precision within this range was 0.4-0.8%. A computer program based on Monte Carlo simulations was developed for the prediction of absorbance precision characteristics under various conditions of measurements

Optimal Operation of Community Energy Storage using Stochastic Gradient Boosting Trees

This paper proposes an algorithm for the optimal operation of community energy storage systems (ESSs) using a machine learning (ML) model by solving a nonlinear programming (NLP) problem iteratively to obtain synthetic data. The NLP model minimizes the network's total energy losses by setting the community ESS's operation points. The optimization model is solved recursively by Monte Carlo simulations in a distribution system with high PV penetration, considering uncertainty in exogenous parameters. Obtained optimal solutions provide the training dataset for a stochastic gradient boosting trees (SGBT) ML algorithm following an imitation learning approach. The predictions obtained from the ML model have been compared to the optimal ESS operation to assess the model's accuracy. Furthermore, the ML model's sensitivity has been tested considering the sampling size and the number of predictors. Results showed a 98% of accuracy for the SGBT model compared to optimal solutions. This accuracy was obtained even after a reduction of 83% in the number of predictors