Forecast Inflows Research Articles

Heat and Mass Transfer of Boundary Layer Jeffrey Ternary Hybrid Nanofluid Flow Over Porous Wedge With Surface‐Catalysed Chemical Reactions: An ANFIS Model

The current study employs machine learning (ML) techniques to investigate the heat and mass transport of Jeffrey ternary hybrid nanofluid (THNF). Various influential factors, including magnetic field, thermal radiation, viscous dissipation, activation energy, thermophoresis, Brownian motion and surface‐catalysed chemical reactions, are considered in the analysis. The porous moving wedge supports and influences the flow pattern. For thermal enhancement, three different nanoparticles, namely, Ag (silver), CuO (copper oxide) and SWCNT (single − walled carbon nanotubes), diluted in the regular fluid (blood). Initially, the mathematically modelled partial differential equations (PDEs) are solved numerically by the help of the shooting method. The suitable nondimensional similarity transformations are implied to convert the dimensional PDEs to nondimensional ordinary differential equations (ODEs). The reduced dimensionless nonlinear coupled ODEs are solved using ode‐45 in MATLAB. The impact of φAg, φCuO, φSWCNT in Cfx, Nux, Shx, is displayed in cone plots. It is found that, on increasing φAg, φCuO, φSWCNT, the heat transfer and mass transfer rate gets enhanced in THNF than the hybrid nanofluid (HNF) and nanofluid (NF). The influence of nondimensional parameters over f″(0), θ′(0), ϕ′(0), G′(0) and H′(0) is displayed in 3D contour plots for THNF and HNF. The radiation parameter (R), Brownian motion parameter (Nb), thermophoresis parameter (Nt) and volume fraction parameters φAg, φCuO, φSWCNT boost the energy transfer rate. Finally, the obtained numerical results are split into training and validation datasets that are used in designing the adaptive neuro‐fuzzy inference system (ANFIS) ML model. Five different ANFIS models are developed with the help of nondimensional parameters affecting f″(0), θ′(0), ϕ′(0), G′(0) and H′(0) to predict the physical quantities of dragging force (Cfx), energy transfer rate (Nux), the rate of mass transport (Shx) and mass fluxes for chemical species and , respectively. The training and checking errors attained the convergence less than 2 × 10−4 and 0.2, respectively, for all the five factors. The present ANFIS models have good balance between fitting the training data and generalising to new data, which can make accurate predictions irrespective of variations. The numerical and predicted ANFIS f″(0), θ′(0), ϕ′(0), G′(0) and H′(0) for training data, checking data and results are demonstrated in regression plots. From regression plots, we can observe that the Pearson correlation coefficient attains the positive correlation with R value closer to one. Hence, the developed ANFIS model shows the minimal error in predictions with high degree of accuracy of forecast in THNF flow.

Flood Forecasting Using Artificial Neural Network

The process of assessing the timing, amount, and period of flood events based on observed features of a river basin is known as flood forecasting. Floods cause lots of damage to properties and create a risk to human life. Flood forecasting is critical for developing appropriate flood risk management strategies, reducing flood hazards, evacuating people from flood-prone areas. The main objective of this study is to apply artificial neural networks for forecasting of river flow in the Deo River, located in Gujarat. Rainfall and discharge are the parameters considered for model development. The developed model is validated to test the accuracy of the model. Trained and validated models are evaluated using performance indices. Six alternative flood prediction models have been developed using ANN. These models are developed based on various training algorithms. A single layer feed forward back-propagation neural network with six different training algorithms (Scaled conjugate gradient, Levenberg Marquardt, Resilient back-propagation, Conjugate gradient, and Cascade forward back propagation, Bayesian regularization) was developed, with 70% of the data used for training and 30% for validation. The created models’ performance is assessed using statistical performance parameters. The best performance was obtained with an ANN model developed using the Cascade forward back-propagation training algorithm, which had a coefficient of correlation (r) of 0.83, a coefficient of determination (R2) of 0.70, and a root mean squared error (RMSE) of 5.58 for training and a coefficient of correlation (r) of 0.89, a coefficient of determination (R2) of 0.70, and a root mean squared error (RMSE) of 7.27 for validation. The forecast inflow is very close to the observed values. This study shows that ANN can be used to successfully predict floods, and the model developed can be used by flood control departments across the country for flood forecasting.

Evaluation of the effective forecast and decision horizon in optimal hydropower generation considering medium-range precipitation forecasts

Abstract This paper presents a rolling horizon control (RHC) model to evaluate the effective forecast horizon (EFH) of 10-day forecast inflows derived from quantitative precipitation forecasts (QPFs) and the effective decision horizon (EDH) for hydropower generation. This paper takes the Huanren hydropower reservoir located in the northeast of China as a case study. Firstly, the 10-day forecast inflows are derived from the QPFs. Then the hydropower generation processes are simulated by the RHC model, and the performances of hydropower generation with different EFHs and EDHs are evaluated, respectively. The results show that: (1) the RHC can adapt to varying conditions by re-optimizing the decisions during the EFH; (2) with the EFH increasing, the hydroelectric reliability increases and the efficiency decreases, while the efficiency and reliability are improved with shortened the EDH.

Analysis of the effects of biases in ensemble streamflow prediction (ESP) forecasts on electricity production in hydropower reservoir management

Abstract. This paper presents an analysis of the effects of biased extended streamflow prediction (ESP) forecasts on three deterministic optimization techniques implemented in a simulated operational context with a rolling horizon test bed for managing a cascade of hydroelectric reservoirs and generating stations in Québec, Canada. The observed weather data were fed to the hydrological model, and the synthetic streamflow subsequently generated was considered to be a proxy for the observed inflow. A traditional, climatology-based ESP forecast approach was used to generate ensemble streamflow scenarios, which were used by three reservoir management optimization approaches. Both positive and negative biases were then forced into the ensembles by multiplying the streamflow values by constant factors. The optimization method's response to those biases was measured through the evaluation of the average annual energy generation in a forward-rolling simulation test bed in which the entire system is precisely and accurately modelled. The ensemble climate data forecasts, the hydrological modelling and ESP forecast generation, optimization model, and decision-making process are all integrated, as is the simulation model that updates reservoir levels and computes generation at each time step. The study focussed on one hydropower system both with and without minimum baseload constraints. This study finds that the tested deterministic optimization algorithms lack the capacity to compensate for uncertainty in future inflows and therefore place the reservoir levels at greater risk to maximize short-term profit. It is shown that for this particular system, an increase in ESP forecast inflows of approximately 5 % allows managing the reservoirs at optimal levels and producing the most energy on average, effectively negating the deterministic model's tendency to underestimate the risk of spilling. Finally, it is shown that implementing minimum load constraints serves as a de facto control on deterministic bias by forcing the system to draw more water from the reservoirs than what the models consider to be optimal trajectories.

Considering precipitation forecasts for real-time decision-making in hydropower operations

This paper presents a new decision-making strategy for hydropower operations to handle uncertainty of forecasting precipitation. This strategy takes into account three basic components: uncertainty of precipitation, operation policies and a risk-evaluation model. In real-time operation, precipitations with different probabilities at different forecasting levels are obtained, and these precipitations are applied to forecast inflows using a hydrological forecasting model. Based on the forecasting inflows, the operation policies and risks with different probabilities are obtained. This study implements China’s Huanren reservoir and medium-term precipitation forecasts from the Global Forecast System to study the efficiency and stability of this strategy.

Assessing the Effect of Using a Preliminary Hydrological Forecast to Increase Spring Flushes in the Lower Reaches of the Volga

The possibility of changing the scheme for regulating the discharge of water from reservoirs in the Volga – Kama cascade by transitioning to a variable drawdown level depending on forecast inflow, as well as lead time and precision of hydrological forecasts of water inflow. A quantitative estimate of increased water flush volume is presented for the second quarter in the lower reaches of the Volga.

Streamflow forecast uncertainty evolution and its effect on real-time reservoir operation

When employing streamflow forecasting in practical applications, such as reservoir operation, one important issue is to deal with the uncertainty involved in forecasting. Traditional studies dealing with the uncertainty in streamflow forecasting have been limited in describing the evolution of forecast uncertainty. This paper proposes a copula-based uncertainty evolution (CUE) model to describe the evolution of streamflow forecast uncertainty. The generated forecast uncertainty series fits the observed series well in terms of observed mean, standard deviation and skewness. Daily flow with forecast uncertainty are simulated and used to determine the effect of forecast uncertainty on real-time reservoir operation of the Three Gorges Reservoir (TGR), China. Results show that using the forecast inflow coupled with the pre-release module for reservoir operation of TGR in flood season cannot increase the flood risk.

Inflow forecasting using Artificial Neural Networks for reservoir operation

Abstract. In this study, multi-layer perceptron (MLP) artificial neural networks have been applied to forecast one-month-ahead inflow for the Ubonratana reservoir, Thailand. To assess how well the forecast inflows have performed in the operation of the reservoir, simulations were carried out guided by the systems rule curves. As basis of comparison, four inflow situations were considered: (1) inflow known and assumed to be the historic (Type A); (2) inflow known and assumed to be the forecast (Type F); (3) inflow known and assumed to be the historic mean for month (Type M); and (4) inflow is unknown with release decision only conditioned on the starting reservoir storage (Type N). Reservoir performance was summarised in terms of reliability, resilience, vulnerability and sustainability. It was found that Type F inflow situation produced the best performance while Type N was the worst performing. This clearly demonstrates the importance of good inflow information for effective reservoir operation.

Inflow Forecasting of a Dam by Neural Network Using Rain Data in Wide Area

At a multipurpose dam, it is necessary to forecast inflow to control flood safely and to operate hydraulic power plant efficiently. In this paper, we propose a method of forecasting the inflow of several hours later by neural network. The correlation is high about the inflow and rain which fell in the dam basin, but it is difficult to forecast by mathematical methods, because the relation is non-linear model. The neural network system can forecast the inflow by learning the past data of inflow and rain in the basin. This system can forecast inflow well after 1 hour or so. However, this system becomes inaccurate rapidly when it tries to forecast inflow at 3 or more hours later, because we use the rain data of the dam basin. Therefore, we also use rain data which is out of the dam basin and in the direction of the windward. The rain data contains information of the rain which will fall at the dam in future. Then, forecast results show that our system is effective.

Real time operation of reservoir based canal irrigation systems

It is proposed that real-time adaptive operation of reservoir based canal irrigation systems be carried out in two phases, a plan phase and operation phase. In the plan phase, biweekly reservoir releases are optimized for a set of target releases and mean inflows. This is done by minimizing a water supply deficit function using dynamic programming. In the operation phase, real-time releases are made according to plan for the current interval. The releases are transferred to the field level after accounting for conveyance losses. At this level, the allocations of available field supplies to competing crops are also optimized. The plan for the remaining intervals of the season is then modified by replacing the inflow for the previous interval with the observed inflow during the interval. The current interval inflow is replaced by the corresponding forecast inflow. The forecasts are obtained from a time series model of weekly inflows. These four steps, namely, (i) releasing supplies from reservoir according to seasonal plan in the current interval, (ii) determining the fraction of the releases available to crops at field level, (iii) allocating the field level supply to competing crops, and (iv) modifying the reservoir release plan for the remaining intervals of the year based on inflows and forecasts, operate sequentially in real-time for each interval. As the season advances, this cycle repeats successively from the first to last intervals of the year. The application of the procedure is demonstrated using data from a case study irrigation project in India.

Real-time multipurpose reservoir operation: a case study

Abstract A real-time operational methodology has been developed for multipurpose reservoir operation for irrigation and hydropower generation with application to the Bhadra reservoir system in the state of Karnataka, India. The methodology consists of three phases of computer modelling. In the first phase, the optimal release policy for a given initial storage and inflow is determined using a stochastic dynamic programming (SDP) model. Streamflow forecasting using an adaptive AutoRegressive Integrated Moving Average (ARIMA) model constitutes the second phase. A real-time simulation model is developed in the third phase using the forecast inflows of phase 2 and the operating policy of phase 1. A comparison of the optimal monthly real-time operation with the historical operation demonstrates the relevance, applicability and the relative advantage of the proposed methodology.