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Babacar M. Ndiaye

Publications and source records attributed to Babacar M. Ndiaye.

9 recordsLinked to original sources
arXiv2026

Planning electric bus systems with solar photovoltaic integration using open transit data: A case study of the Dakar BRT

Electrification of urban bus systems is expected to accelerate due to its clear environmental benefits. However, effective electric bus deployment requires planning tools that can support decision-making on electrification strategies while balancing operational feasibility, costs, environmental benefits, and impacts on the local electricity grid. Such planning depends on the availability of detailed transit data, which remain scarce in many cities, particularly in developing countries. To address this gap, we present GTFS4EV, an open-source framework that uses publicly available General Transit Feed Specification (GTFS) data to simulate bus operations and evaluate electrification scenarios. The framework provides quantitative insights across multiple dimensions. It estimates the minimum onboard battery capacity required for each bus, alongside charging infrastructure needs, economic and environmental impacts, and the potential for solar photovoltaic integration. We apply the framework to the Dakar Bus Rapid Transit system, comparing three charging strategies ("Depot only", "Terminal and depot" and "Terminal only") across different levels of PV capacities. Results show that the "Terminal and depot" strategy reduces the minimum onboard battery capacity from 285 to 57 kWh per bus, and the maximum charging load from 5.3 to 1.5 MW relative to the "Depot only" strategy. The "Terminal only" also provides benefits compared to the "Depot only", although it requires larger onboard battery capacities (155 kWh). Moreover, combining opportunity charging with solar photovoltaics reduces the charging costs by up to 46% relative to grid charging only. The study demonstrates how GTFS data can be leveraged to support electric bus planning and photovoltaic integration in data-scarce contexts.

eess.SY↗
arXiv2026

Stochastic inflows and seasonal water value at Manantali

Seasonal hydropower operation requires release decisions that account for uncertain future inflows and the opportunity cost of stored water. We assess the Modelling and Optimisation of Stochastic Seasonal Hydropower Storage (MOSSHOOS) framework for the Manantali reservoir using 660 monthly energy-inflow observations from 1961--2015. The framework combines multiscale inflow representation, blocked resampling and scenario reduction with stochastic dual dynamic programming, and uses a seasonal square-root diffusion and Hamilton--Jacobi--Bellman formulation as a continuous-time interpretation of marginal water value. Scenario reduction improves the energy score by 47.6\% relative to independent monthly sampling, but the marginal distribution is rejected by a Kolmogorov--Smirnov test and the lower-tail p10 error remains 103.9\%, indicating inadequate dry-tail calibration. Consistent with that limitation, 50 paired Monte Carlo evaluations show no detectable cost or deficit advantage of the stochastic policy over its deterministic-equivalent counterpart at the archived operating configuration. As an independent benchmark, the repaired open-source \texttt{plan4res} seasonal storage valuation chain yields a dominant marginal value of 81.66~USD/MWh, within 3\% of the MOSSHOOS mean of 79.25~USD/MWh. These results support a coherent stochastic inflow-to-water-value methodology while showing that hydrological tail representation, rather than economic scaling, is the principal limitation to address before operational application.

math.OC↗
arXiv2026

InFlow: entropic stochastic dual dynamic programming with HJB cross-certification for seasonal energy storage

Seasonal reservoir operation requires optimization methods that remain reliable under strongly periodic and uncertain inflow dynamics. We present InFlow, a verification-oriented framework combining periodic stochastic dual dynamic programming (SDDP), entropic robustness, control-space Bellman annealing, and an independently discretized Hamilton-Jacobi-Bellman (HJB) benchmark. The entropic transition operator is interpreted as a KL-penalized worst-case expectation and linked to fixed-radius KL ambiguity sets through Lagrange duality. We establish a uniform soft-Bellman approximation bound and prove the validity of Gibbs-tilted SDDP cuts. The benchmark relies on a normalized seasonal Cox-Ingersoll-Ross inflow process whose positivity condition fails during the driest weeks, requiring explicit treatment of the degenerate boundary. Independent verification shows strong agreement between HJB storage gradients and HiGHS storage-balance duals, with water-value correlations of 0.995 and 0.996 for the standard and exact-cut-only implementations. Refining the HJB grid from 61^2 to 81^2 states changes the annual mean water value by only 0.6% and the peak value by less than 0.1%. Out-of-sample simulations based on 3,000 independent trajectories show that a robustness level of gamma=5 increases nominal mean cost by 4.6% while reducing CVaR90 by 11.2%. Under a dry-season stress scenario with inflows reduced by 40%, the mean-cost difference becomes statistically insignificant whereas CVaR90 improves by 11.5%. A robustness sweep confirms monotonicity of the risk-adjusted value. Finally, a two-reservoir cascade reproduces grid-oracle marginal water values with correlation 0.999 and recovers the predicted upstream-to-downstream value ratio of two. InFlow is intended as a transparent methodological benchmark rather than a calibrated operational reservoir model.

math.OC↗
arXiv2026

From Daily Fluctuations to Annual Hydrological Cycles: A Wavelet-Based Analysis of Nonstationary Seasonality in Senegal River Hydropower Inflows

This study presents a reproducible framework combining Fourier and wavelet analysis to examine the seasonality of daily inflows at three sites on the Senegal River (Bafing Makana, Felou, Gouina), based on 65,631 daily observations spanning nearly 60 years (1961-2020). Using harmonic regression, Welch spectral analysis, stationary wavelet decomposition, Morlet continuous wavelet transforms, trend and change-point tests, and cross-wavelet coherence, the authors show that the annual cycle corresponds mainly to the D8 detail level (roughly 256-512 days), rather than a level-4 approximation. The Haar wavelet was selected as the best fit among four families tested. The D8 component accounts for 18.85-19.89% of total energy, while Fourier harmonic models explain 73.3-78.7% of daily inflow variability. The average seasonal peak occurs in September (around days 252-254), and annual coherence across the three sites is exceptionally high (0.988-0.999), indicating a highly synchronized regional hydrological cycle. No robust monotonic trend was found in seasonality strength, amplitude, or peak timing. However, Pettitt tests detected a statistically significant shift in amplitude or mean inflow around 1971-1976, consistent with a regional regime transition, though confirming this would require independent rainfall and dam-operation data. The framework offers a transparent basis for generating inflow scenarios within MOSSHOOS-Plan4RES and is transferable to other data-scarce hydropower systems.

stat.AP↗
arXiv2024

Mobility-based Traffic Forecasting in a Multimodal Transport System

We study the analysis of all the movements of the population on the basis of their mobility from one node to another, to observe, measure, and predict the impact of traffic according to this mobility. The frequency of congestion on roads directly or indirectly impacts our economic or social welfare. Our work focuses on exploring some machine learning methods to predict (with a certain probability) traffic in a multimodal transportation network from population mobility data. We analyze the observation of the influence of people's movements on the transportation network and make a likely prediction of congestion on the network based on this observation (historical basis).

physics.soc-ph↗
arXiv2024

An optimal control study for a two-strain SEIR epidemic model with saturated incidence rates and treatment

This work will study an optimal control problem describing the two-strain SEIR epidemic model. The studied model is in the form of six nonlinear differential equations illustrating the dynamics of the susceptibles and the exposed, the infected, and the recovered individuals. The exposed and the infected compartments are each divided into two sub-classes representing the first and the second strain. The model includes two saturated rates and two treatments for each strain. We begin our study by showing the well-posedness of our problem. The basic reproduction number is calculated and depends mainly on the reproduction numbers of the first and second strains. The global stability of the disease-free equilibrium is fulfilled. The optimal control study is achieved by using the Pontryagin minimum principle. Numerical simulations have shown the importance of therapy in minimizing the infection's effect. By administrating suitable therapies, the disease's severity decreases considerably. The estimation of parameters as well as a comparison study with COVID-19 clinical data are fulfilled. It was shown that the mathematical model results fits well the clinical data.

q-bio.PE↗
arXiv2020

Analysis of COVID-19 evolution in Senegal: impact of health care capacity

We consider a compartmental model from which we incorporate a time-dependent health care capacity having a logistic growth. This allows us to take into account the Senegalese authorities response in anticipating the growing number of infected cases. We highlight the importance of anticipation and timing to avoid overwhelming that could impact considerably the treatment of patients and the well-being of health care workers. A condition, depending on the health care capacity and the flux of new hospitalized individuals, to avoid possible overwhelming is provided. We also use machine learning approach to project forward the cumulative number of cases from March 02, 2020, until 1st December, 2020.

q-bio.PE↗
arXiv2020

Impact studies of nationwide measures COVID-19 anti-pandemic: compartmental model and machine learning

In this paper, we deal with the study of the impact of nationwide measures COVID-19 anti-pandemic. We drive two processes to analyze COVID-19 data considering measures. We associate level of nationwide measure with value of parameters related to the contact rate of the model. Then a parametric solve, with respect to those parameters of measures, shows different possibilities of the evolution of the pandemic. Two machine learning tools are used to forecast the evolution of the pandemic. Finally, we show comparison between deterministic and two machine learning tools.

q-bio.PE↗