LP-based Model Research Articles

Applying load profiles propagation to machine learning based electrical energy forecasting

Electrical energy generation represents an economical and environmental challenge requiring an optimal control of the production process. An accurate modeling of the electrical energy is essential to develop efficient forecasting systems. To achieve this goal, this paper introduces an innovative load forecasting approach using Load Profiles (LPs). First, the power consumption in Algeria is analyzed to detect the different factors affecting the demand. Then, the fluctuation of the seasonal data is applied through hourly temperature profiles. The LP-based forecasting is performed using three levels (annual, weekly and daily) LP-propagation. Short-term and mid-term load forecasting models were developed using multiple Artificial Intelligence techniques. Among them, a two-dimensional Convolutional Neural Network (CNN) used here for the first time in load forecasting. The resulting prediction accuracies of both the Artificial Intelligence (AI)-based and LP-based models were considerably high, producing (MAPE= 0.80%, RMSE= 75.57MW, Willmott's Index (WI) = 0.99) for the two-dimensional CNN. Compared to (MAPE= 3.86%, RMSE= 372.68 MW, WI= 0.95) for the LP-propagation technique.

Partitioned event graph: formalizing LP-based modelling of parallel discrete-event simulation

Logical process (LP) is a modelling paradigm widely used in parallel discrete-event simulation (PDES). However, effective methods for formalizing LP-based modelling of PDES are lacking. This prevents an unambiguous, platform-independent description of LP-based models. We present a formalism named partitioned event graph (PEG) as a solution. PEG extends classical event graph formalism towards a formal specification for LP-based PDES models. We map between PEG and LP-based models, define the structural operational semantics (SOS) in a timed-labelled transition system, and discuss the Wallclock time-based execution. We propose a PEG-based model transformation framework for PDES, which has three model representation phases and distinguishes amongst four kinds of personnel roles. Finally, we present a domain-specific language (DSL) for the PDES of a Lotka–Volterra system and obtain preliminary parallel simulation results using YinHe Simulation Utilities for Parallel Environment (YHSUPE). The case study shows that the PEG-based framework not only effectively transforms a DSL into the LP paradigm, but will also result in efficient parallel simulation on a specific platform. In summary, by setting out specific characteristics for event scheduling and state space partition in the LP paradigm, PEG provides a formal method for model behaviour analysis and cross-platform model transformation.

Solving linear and quadratic programs with an analog circuit

We present the design of an analog circuit which solves linear programming (LP) or quadratic programming (QP) problem. In particular, the steady-state circuit voltages are the components of the LP (QP) optimal solution. The paper shows how to construct the circuit and provides a proof of equivalence between the circuit and the LP (QP) problem. The proposed method is used to implement an LP-based Model Predictive Controller by using an analog circuit. Simulative and experimental results show the effectiveness of the proposed approach.

Yield Estimation for a Single Purpose Multi-Reservoir System Using LP Based Yield Model

Application of optimization techniques for determining the optimal operation policy for reservoir is a major area in water resources planning and management. Linear programming, ruled by evolution techniques, has become popular for solving optimization problems in diversified fields of science. An LP-based yield model (YM) has been used to reevaluate the annual yield available from the reservoirs for irrigation. This paper extends the basic yield model and presents a yield model for a multiple-reservoir system consisting of single-purpose reservoirs. Optimum yield of reservoirs system is calculated by yield model. The objective is to achieve prespecified reliability for irrigation and to incorporate an allowable deficit in the annual irrigation target. The yield model is applied to a system of two reservoirs in theManarRiverinIndia. This model can act as a better screening tool in planning by providing outputs that can be very useful in improving the efficiency and accuracy of detailed analysis methods such as simulation.

A Study on Restoration of Bone-Conducted Speech in Noisy Environments with LP-based Model and Gaussian Mixture Model

A Study on Restoration of Bone-Conducted Speech in Noisy Environments with LP-based Model and Gaussian Mixture Model

Crop succession requirements in agricultural production planning

A method is proposed to write crop succession requirements as linear constraints in an LP-based model for agricultural production planning. Crop succession information is given in the form of a set of inadmissible successions of crops. The decision variables represent the areas where a certain admissible sequence of crops is cultivated. The number of decision variables may be reduced by forming suitable combinations of crop sequences. For this purpose, an algorithm is presented. Also, multi-year linear programming models for farm production planning containing crop succession constraints are considered. It is shown that, under some regularity conditions, a stationary cropping plan is an optimal solution of such a model. Finally, it is discussed how to determine, given a collection of inadmissible sequences, crop sequences which are inadmissible but do not contain inadmissible subsequences. The length of the longest of these sequences determines the length of the crop sequences taken into account in the model.

An Introductory Tutorial on Stochastic Linear Programming Models

Linear programming is a fundamental planning tool. It is often difficult to precisely estimate or forecast certain critical data elements of the linear program. In such cases, it is necessary to address the impact of uncertainty during the planning process. We discuss a variety of LP-based models that can be used for planning under uncertainty. In all cases, we begin with a deterministic LP model and show how it can be adapted to include the impact of uncertainty. We present models that range from simple recourse policies to more general two-stage and multistage SLP formulations. We also include a discussion of probabilistic constraints. We illustrate the various models using examples taken from the literature. The examples involve models developed for airline yield management, telecommunications, flood control, and production planning.

Comparison of the Prediction Models for the L5/S1 Compressive Forces under Varying Asymmetric Lifting Conditions

In this study, comparisons were made among three representative methods for predicting compressive forces on the lumbosacral disc: LP-based model, double LP-based model and EMG-assisted model. Two subjects simulated lifting tasks that were frequently performed in the refractories industry in Korea, in which vertical distance, frontal plane horizontal distance, and weight of load were varied. To calculate the L5/S1 compressive forces, EMG signals from six trunk muscles were measured and postural data were recorded using the Motion Analysis System. The EMG-assisted model was shown to reflect well all three factors considered here. On the other hand, the compressive forces of the two LP-based models were significantly affected only by weight of load. Furthermore, relatively low correlation was observed between the compressive force of the EMG-assisted model and the 1991 NIOSH lifting index(LI), while highly positive correlation was observed between that of the two LP-based models and LI.

An improvement of the LP-based Aucamp model for the solution of the multi-item lot-sizing capacity-constrained problem

Abstract This paper provides a summary of the results of some studies regarding the formulation of the master production schedule. The objective of the research was to identify the optimal range of certain aggregated production planning models applied to different production situations. This paper is divided into four sections:

Forest monitoring and harvesting policies

This paper considers the problem of what information must be measured in a forest management model in order to generate optimal feedback harvesting policies. Together with this question, the paper also addresses the issue of how LP-based models can be embedded within a dynamic programming framework, so that feedback rather than open-loop decisions can be determined. The appendix to the paper presents a more general system-theoretic framework within which forest management may be studied. Issues of surprise, response to unknown disturbances, and robustness of policies are examined and a program for systematically investigating such management questions is outlined.