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The non-stationary, complex, and non-linear characteristics of streamflow time series have a significant impact on the simulation results of the conventional hydrological forecasting models. To improve the performances, this paper develops an enhanced machine learning model for streamflow time series forecasting, where the twin support vector regression (TSVR) is combined with singular spectrum analysis (SSA) and grey wolf optimizer (GWO). Specially, the SSA method is set as the data preprocessing tool for pattern identification; the TSVR model is set as the basic forecasting module for each pattern and the GWO method is used as the optimizer to select feasible parameter combination. Multi-step-ahead streamflow forecasting tasks are used to examine the feasibility and predictability of the proposed model. The results indicate that the proposed model can yield superior results compared with the traditional forecasting models. Thus, a robust and reliable tool is provided for streamflow time series forecasting under uncertainty.

Generally, accurate hydrological forecasting information plays an increasingly important role in promoting the comprehensive benefit of hydropower reservoirs. With satisfying generalization ability and search rate, the extreme learning machine (ELM), a famous single-layer feedforward neural network, has been widely used to address regression and classification problem. However, the standard ELM method often falls into second-best solutions with a high probability due to the random assignments of network parameters. In order to overcome this problem, this article aims at developing a hybrid model for monthly runoff time series forecasting. In the hybrid method, an effective swarm intelligence method, grey wolf optimizer (GWO), is adopted to optimize the input-hidden weights and hidden biases of the ELM method; and then the Moore-Penrose generalized inverse method is adopted to determine the hidden-output weights. The world's largest hydropower reservoir, Three Gorges, is chosen to compare the performances of various forecasting methods. Based on the simulation results, the presented method outperforms several traditional forecasting methods (like artificial neural network and support vector machine) in several quantitative indexes. Thus, a novel alternative is presented to predict the nonlinear hydrological time series in China.

As one of the most important renewable energy, hydropower is often asked to satisfy the load demand of power system at peak periods. Thus, the optimal operation of hydropower system is modelled to minimize the standard deviation of the residual load series obtained by subtracting the total power outputs of all the involved hydropower plants from the original load curve. Hence, this paper develops an improved grey wolf optimizer (IGWO) to effectively address the complex constrained optimization problem. In the proposed method, the quasi-oppositional learning is used to enhance the convergence rate of the swarm; the elite mutation operator is used to increase the probability of escaping from local optima; the elastic-ball strategy and heuristic constraint handling method are used to help infeasible individuals rebound to feasible space. Numerical experiments of 12 classical test functions demonstrate the feasibility of the IGWO method in the global optimization problems. Then, the developed method is applied to solve the optimal operation of two cascade hydropower systems. The results indicate that the proposed method outperforms several traditional methods in smoothing the peak loads of power system. To sum up, an effective solution tool is provided for the hydropower system operation optimization problem.

Reliable load time series forecasting plays an important role in guaranteeing the safe and stable operation of modern power system. Due to the volatility and randomness of electricity demand, the conventional forecasting method may fail to effectively capture the dynamic change of load curves. To satisfy this practical necessity, the goal of this paper is set to develop a practical machine learning model based on feature selection and parameter optimization for short-term load prediction. In the proposed model, the ensemble empirical mode decomposition is used to divide the original loads into a sequence of relatively simple subcomponents; for each subcomponent, the support vector machine is chosen as the basic predictor where the real-valued cooperation search algorithm (CSA) is used to seek the best model hyperparameters, while the binary-valued CSA is set as the feature selection tool to determine the candidate input variables; finally, the aggregation of all the submodules’ outputs forms the final forecasting result. The presented method is assessed by short-term load data from four provincial-grid dispatching centers in China. The experiments demonstrate that the proposed model can provide better results than several conventional models in short-term load prediction, while the emerging CSA method is an effective tool to determine the parameter combinations of machine learning method.

With the merits of superior performance and easy implementation, the harmony search (HS), a famous population-based evolutionary method, has been widely adopted to resolve global optimization problems in practice. However, the standard HS method still suffers from the defects of premature convergence and local stagnation in the complex multireservoir operation problem. Thus, this study develops an enhanced harmony search (EHS) method to improve the HS’s search ability and convergence rate, where adaptive parameter adjustment strategy is used to enhance the global search performance of the swarm, while the elite-learning evolutionary mode is used to improve the converge trajectory of the population. To verify its practicability, EHS is applied to solve numerical optimization and multireservoir operation problems. The results show that EHS can produce better results than several existing methods in different cases. For instance, the mean objective of EHS is improved by about 23.9%, 28.7% and 26.8% compared with particle swarm optimization, differential evolution and gravitational search algorithm in 1998–1999 typical runoff case. Hence, an effective optimizer is developed for sustainable ecological operation of cascade hydropower reservoirs in river ecosystem.

In recent years, growing peak pressure is posing a huge challenge for the operators of electrical power systems. As the most important clean renewable energy, hydropower is often advised as a response to the peak loads in China. Thus, a novel hybrid sine cosine algorithm (HSCA) is proposed to deal with the complex peak operation problem of cascade hydropower reservoirs. In HSCA, the elite-guide evolution strategy is embedded into the standard sine cosine algorithm to improve the convergence rate of the swarm. The Gaussian local search strategy is used to increase the diversity of the population. The random mutation operator is adopted to enhance the search capability of the individuals in the evolutionary process. The proposed method is applied to solve the complex peak operation problem of two hydropower systems. The simulations indicate that in different cases, HSCA can generate the scheduling results with higher quality than several benchmark methods. Hence, this paper provides a feasible method for the complex peak operation problem of cascade hydropower reservoirs.

With the increasingly serious energy crisis and environmental pollution, the short-term economic environmental hydrothermal scheduling (SEEHTS) problem is becoming more and more important in modern electrical power systems. In order to handle the SEEHTS problem efficiently, the parallel multi-objective genetic algorithm (PMOGA) is proposed in the paper. Based on the Fork/Join parallel framework, PMOGA divides the whole population of individuals into several subpopulations which will evolve in different cores simultaneously. In this way, PMOGA can avoid the wastage of computational resources and increase the population diversity. Moreover, the constraint handling technique is used to handle the complex constraints in SEEHTS, and a selection strategy based on constraint violation is also employed to ensure the convergence speed and solution feasibility. The results from a hydrothermal system in different cases indicate that PMOGA can make the utmost of system resources to significantly improve the computing efficiency and solution quality. Moreover, PMOGA has competitive performance in SEEHTS when compared with several other methods reported in the previous literature, providing a new approach for the operation of hydrothermal systems.

The progressive optimality algorithm (POA) is commonly used to identify optimal hydropower operation schedules in China. However, POA may not converge within a reasonable time for large and complex problems because its computational burden grows exponentially with the expansion of system scale. In order to effectively alleviate the dimensionality problem of POA, an improved POAvariant called orthogonal progressive optimality algorithm (OPOA) is introduced in this paper. In the OPOA, an orthogonal experimental design is used to replace the exhaustive combinatorial evaluation at each POA two-stage subproblem. The theoretical analysis shows that POA and OPOA have exponential and approximately polynomial growth in computational complexity, respectively. The proposed method is applied to a large-scale multireservoir system located on the Wu River in China. The results indicate that, compared with POA, OPOA can remarkably enhance the computing efficiency in different cases, showing its practicability and feasibility for multireservoir system operation. [ABSTRACT FROM AUTHOR]

Reservoir monthly inflow is rather important for the security of long-term reservoir operation and water resource management. The main goal of the present research is to develop forecasting models for the reservoir monthly inflow. In this paper, artificial neural networks (ANN) and support vector machine (SVM) are two basic heuristic forecasting methods, and genetic algorithm (GA) is employed to choose the parameters of the SVM. When forecasting the monthly inflow data series, both approaches are inclined to acquire relatively poor performances. Thus, based on the thought of refined prediction by model combination, a hybrid forecasting method involving a two-stage process is proposed to improve the forecast accuracy. In the hybrid method, the ANN and SVM are, first, respectively implemented to forecast the reservoir monthly inflow data. Then, the processed predictive values of both ANN and SVM are selected as the input variables of a newly-built ANN model for refined forecasting. Three models, ANN, SVM, and the hybrid method, are developed for the monthly inflow forecasting in Xinfengjiang reservoir with 71-year discharges from 1944 to 2014. The comparison of results reveal that three models have satisfactory performances in the Xinfengjiang reservoir monthly inflow prediction, and the hybrid method performs better than ANN and SVM in terms of five statistical indicators. Thus, the hybrid method is an efficient tool for the long-term operation and dispatching of Xinfengjiang reservoir. [ABSTRACT FROM AUTHOR]

Accurate daily runoff forecasting is of great significance for the operation control of hydropower station and power grid. Conventional methods including rainfall-runoff models and statistical techniques usually rely on a number of assumptions, leading to some deviation from the exact results. Artificial neural network (ANN) has the advantages of high fault-tolerance, strong nonlinear mapping and learning ability, which provides an effective method for the daily runoff forecasting. However, its training has certain drawbacks such as time-consuming, slow learning speed and easily falling into local optimum, which cannot be ignored in the real world application. In order to overcome the disadvantages of ANN model, the artificial neural network model based on quantum-behaved particle swarm optimization (QPSO), ANN-QPSO for short, is presented for the daily runoff forecasting in this paper, where QPSO was employed to select the synaptic weights and thresholds of ANN, while ANN was used for the prediction. The proposed model can combine the advantages of both QPSO and ANN to enhance the generalization performance of the forecasting model. The methodology is assessed by using the daily runoff data of Hongjiadu reservoir in southeast Guizhou province of China from 2006 to 2014. The results demonstrate that the proposed approach achieves much better forecast accuracy than the basic ANN model, and the QPSO algorithm is an alternative training technique for the ANN parameters selection. [ABSTRACT FROM AUTHOR]