Water Quality Indicator Interval Prediction in Wastewater Treatment Process Based on the Improved BES-LSSVM Algorithm

This paper proposes a novel interval prediction method for effluent water quality indicators (including biochemical oxygen demand (BOD) and ammonia nitrogen (NH3-N)), which are key performance indices in the water quality monitoring and control of a wastewater treatment plant. Firstly, the effluent data regarding BOD/NH3-N and their necessary auxiliary variables are collected. After some basic data pre-processing techniques, the key indicators with high correlation degrees of BOD and NH3-N are analyzed and selected based on a gray correlation analysis algorithm. Next, an improved IBES-LSSVM algorithm is designed to predict the BOD/NH3-N effluent data of a wastewater treatment plant. This algorithm relies on an improved bald eagle search (IBES) optimization algorithm that is used to find the optimal parameters of least squares support vector machine (LSSVM). Then, an interval estimation method is used to analyze the uncertainty of the optimized LSSVM model. Finally, the experimental results demonstrate that the proposed approach can obtain high prediction accuracy, with reduced computational time and an easy calculation process, in predicting effluent water quality parameters compared with other existing algorithms.

Application of Interval Predictor Model Into Model Predictive Control

In this paper, interval prediction model is studied for model predictive control (MPC) strategy with unknown but bounded noise. After introducing the family of models and some basic information, some computational results are presented to construct interval predictor model, using linear regression structure whose regression parameters are included in a sphere parameter set. A size measure is used to scale the average amplitude of the predictor interval, then one optimal model that minimizes this size measure is efficiently computed by solving a linear programming problem. The active set approach is applied to solve the linear programming problem, and based on these optimization variables, the predictor interval of the considered model with sphere parameter set can be directly constructed. As for choosing a fixed non-negative number in our given size measure, a better choice is proposed by using the Karush-Kuhn-Tucker (KKT) optimality conditions. In order to apply interval prediction model into model predictive control, the midpoint of that interval is substituted in a quadratic optimization problem with inequality constrained condition to obtain the optimal control input. After formulating it as a standard quadratic optimization and deriving its dual form, the Gauss-Seidel algorithm is applied to solve the dual problem and convergence of Gauss-Seidel algorithm is provided too. Finally simulation examples confirm our theoretical results.

Short-Term Photovoltaic Power Interval Prediction Based on the Improved Generalized Error Mixture Distribution and Wavelet Packet -LSSVM

The periodicity and non-stationary nature of photovoltaic (PV) output power make the point prediction result contain very little information, increase the difficulty of describing the prediction uncertainty, and it is difficult to ensure the most efficient operation of the power system. Effectively predicting the PV power range will greatly improve the economics and stability of the grid. Therefore, this paper proposes an improved generalized based on the combination of wavelet packet (WP) and least squares support vector machine (LSSVM) to obtain higher accuracy point prediction results. The error mixed distribution function is used to fit the probability distribution of the prediction error, and the probability prediction is performed to obtain the prediction interval. The coverage rate and average width of the prediction interval are used as indicators to evaluate the prediction results of the interval. By comparing with the results of conventional methods based on normal distribution, at 95 and 90% confidence levels, the method proposed in this paper achieves higher coverage while reducing the average bandwidth by 5.238 and 3.756%, which verifies the effectiveness of the proposed probability interval prediction method.

Short term wind power interval prediction based on VMD and improved BLS

Aiming at the problem of wind power interval prediction, a short-term wind power interval prediction model based on VMD and improved BLS is proposed. Firstly, the complex wind power time series are decomposed by variational mode decomposition to reduce the non stationarity of wind power. Then an improved broad learning system (BLS) is established to predict the power and error of each component, and a weight is given to the prediction error of each component. The sparrow search algorithm (SSA) is used to optimize the weight, and the width of the prediction interval is obtained by adding the power and error prediction values. The experimental data show that the proposed model improves the accuracy of prediction interval.

Robust Model Predictive Control for Energy Management of Isolated Microgrids Based on Interval Prediction

With the integration of Renewable Energy Resources (RERs), the Day-Ahead (DA) scheduling for the optimal operation of the integrated Isolated Microgrids (IMGs) may not be economically optimal in real time due to the prediction errors of multiple uncertainty sources. To compensate for prediction error, this paper proposes a Robust Model Predictive Control (RMPC) based on an interval prediction approach to optimize the real-time operation of the IMGs, which diminishes the influence from prediction error. The rolling optimization model in RMPC is formulated into the robust model to schedule operation with the consideration of the price of robustness. In addition, an Online Learning (OL) method for interval prediction is utilized in RMPC to predict the future information of the uncertainties of RERs and load, thereby limiting the uncertainty. A case study demonstrates the effectiveness of the proposed with the better matching between demand and supply compared with the traditional Model Predictive Control (MPC) method and Hard Charging (HC) method.