The scope of this paper is focused on the multidimensional poverty problem in Jordan. Household expenditure and income surveys provide data that are used for identifying and measuring the poverty status of Jordanian households. However, carrying out such surveys is hard, time consuming, and expensive. Machine learning could revolutionize this process. The contribution of this work is the proposal of an original machine learning approach to assess and monitor the poverty status of Jordanian households. This approach takes into account all the household expenditure and income surveys that took place since the early beginning of the new millennium. This approach is accurate, inexpensive, and makes poverty identification cheaper and much closer to real-time. Data preprocessing and handling imbalanced data are major parts of this work. Various machine learning classification models are applied. The LightGBM algorithm has achieved the best performance with 81% F1-Score. The final machine learning classification model could transform efforts to track and target poverty across the country. This work demonstrates how powerful and versatile machine learning can be, and hence, it promotes for adoption across many domains in both the private sector and government.
A model predictive control strategy for a concentrated solar thermal power plant is proposed. Design of the proposed controller is based on an estimated linear time-invariant state space model around a nominal operating point. The model is estimated directly from inputoutput data using the subspace identification method and taking into account the frequency response of the plant. Input-output data are obtained from a nonlinear distributed parameter model of a plant rather than the plant itself. Effectiveness of the proposed control strategy in terms of tracking and disturbance rejection is evaluated through two different scenarios created in a nonlinear simulation environment.
This paper improves a recently proposed gain scheduling predictive control strategy on the ACUREX distributed solar collector field at the Plataforma Solar de Almería. Measured disturbances are an integral part of the plant and while simple classical, series and parallel, feedforward approaches have been proposed and used extensively in the literature, the proposed approach incorporates a feedforward systematically into the predictive control strategy by including the effects of the measured disturbances of the ACUREX plant into the predictions of future outputs. Models of the measured disturbances are estimated around a family of operating points from input-output data and using a subspace identification method while taking into account the frequency response of the plant. Input-output data are obtained from a validated nonlinear simulation model of the plant rather than the plant itself. The nonlinear simulation model is validated against measured data obtained from the ACUREX plant and the effectiveness of the proposed control approach is evaluated in the same nonlinear simulation environment. The paper also considers the impact of incorporating the future behaviour of a measured disturbance along a given prediction horizon, a theme which has received little attention in the literature.
This paper improves a recently proposed twolayer hierarchical control strategy for the ACUREX plant at the Plataforma Solar de Almería. Improvements target the lower layer of the two-layer hierarchical control strategy. Two alternative systematic approaches are proposed and evaluated. The two approaches take explicit account of the measured disturbances. Improvements are illustrated by way of some simulation scenarios and measured data from the ACUREX plant.
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