Solar radiation prediction using wavelet decomposition

Nowadays, a substantial part of the agricultural production takes place in greenhouses, which enable to tune the crop growing by modifying, artificially, the environmental conditions and the plant’s nutrition. The main goal is to optimise the balance between the production economic return and the operation costs of the climate actuators. Severe environment and market restrictions jointly with an increasing tendency of the fuel price motivate the development of more “intelligent” energy regulators. In order to formulate the best options for a production plan, this type of artificial supervisors must be able to formulate close predictions on a large set of variables. Considering, for instance, the air temperature control inside a greenhouse, the system must be able to close predict the evolution of the solar radiation since this is the exogenous variable which most influences the thermal load during the day. In this paper, an artificial neural network, in conjunction with a wavelet decomposition strategy, is used for forecasting, an hour ahead, the instantaneous solar radiation energy density sampled at one minute interval. The results obtained from this work encourage further exploitation of this kind of signal processing techniqu

Case-based reasoning for meta-heuristics self-parameterization in a multi-agent scheduling system

A novel agent-based approach to Meta-Heuristics self-configuration is proposed in this work. Meta-heuristics are examples of algorithms where parameters need to be set up as efficient as possible in order to unsure its performance. This paper presents a learning module for self-parameterization of Meta-heuristics (MHs) in a Multi-Agent System (MAS) for resolution of scheduling problems. The learning is based on Case-based Reasoning (CBR) and two different integration approaches are proposed. A computational study is made for comparing the two CBR integration perspectives. In the end, some conclusions are reached and future work outlined

Hardware-in-the-loop control using the particle swarm optimisation

In the last two decades, evolutionary based algorithms have proved to be an important tool in solving optimisation problems in many disciplinary areas namely in control system design. However one of its limitations, for some type of applications, is the usually high computational load required, which restricts its use for on-line control. This paper proposes the use of a stochastic search algorithm, known as particle swarm, as an optimisation tool for an on-line predictive control of a custom made thermodynamic system. Preliminary results are presented

Real-time control of a laboratory heat exchanger using the particle swarm optimisation algorithm

In the past decade, evolutionary based algorithms have been a popular research theme in many disciplinary areas like control systems. Although, due to the computational load required, this type of algorithms usually are applied off-line. In this paper, a stochastic search algorithm known as particle swarm is used as an optimisation tool for on-line control of a custom made laboratory thermodynamic system

Greenhouse air temperature control using the particle swarm optimisation algorithm

The particle swarm optimisation algorithm is proposed as a new method to design a model based predictive controller subject to restrictions. Its performance is compared with the one obtained by using a genetic algorithm for the environmental temperature control of a greenhouse. Controller outputs are computed in order to optimise future behaviour of the greenhouse environment, regarding set-point tracking and minimisation of the control effort over a prediction horizon of one hour with a one-minute sampling period

Greenhouse air temperature modelling

This paper describes two implementation approaches for modelling the air temperature of an automated greenhouse located in the campus of the University of Trás-os- Montes e Alto Douro. Linear models, based in the discretization of the heat transfer physical laws, and non-linear neural networks models are used. These models are describes as functions of the outside climate and control actions performed for heating and cooling. Results are presented to illustrate the performance of each model in the simulation and prediction of the greenhouse air temperature. The data used to compute the simulation models was collected with a PC-based acquisition and control system using a sampling time interval of 1 minute.The authors appreciate the support of the Portuguese Foundation for Science and Technology (FCT) through the project MGS/ 33906/2000

On-line control using the particle swarm optimisation algorithm

In the last two decades, evolutionary based algorithms have proved to be an important tool in solving optimisation problems in many disciplinary areas, namely in control system design. However one of its limitations for some type of applications is the usually high computational load required, which restricts its use for on-line control. This paper proposes the use of a stochastic search algorithm, known as particle swarm, as an optimisation tool for an on-line model predictive control of a custom made laboratory thermodynamic system. Preliminary results are presented

An integrative approach for codon repeats evolutionary analyses

The relationship between genome characteristics and several human diseases has been a central research goal in genomics. Many studies have shown that specific gene patterns, such as amino acid repetitions, are associated with human diseases. However, several open questions still remain, such as, how these tandem repeats appeared in the evolutionary path or how they have evolved in orthologous genes of related organisms. In this paper, we present a computational solution that facilitates comparative studies of orthologous genes from various organisms. The application uses various web services to gather gene sequence information, local algorithms for tandem repeats identification and similarity measures for gene clustering.publishe

Fatores de sucesso dos projetos de tecnologias e sistemas de informação – uma revisão de literatura

Information Systems projects are often complex enterprises, since they involve the adoption of information technologies in organizational contexts. As such, they should be carefully managed considering the various aspects that influence their success. This paper presents a literature review focused on the success factors of information technology and information systems projects.Os projetos de tecnologias e sistemas de informação são frequentemente empreendimentos complexos, dado envolverem a adoção de tecnologias da informação em contextos organizacionais. Como tal, devem ser geridos com rigor e tendo por referência os diversos aspetos influenciadores dos seus resultados. Neste artigo é apresentado o resultado de uma revisão de literatura focada nos fatores de sucesso dos projetos de tecnologias e sistemas de informação

Ant-Balanced multiple traveling salesmen: ACO-BmTSP

A new algorithm based on the ant colony optimization (ACO) method for the multiple traveling salesman problem (mTSP) is presented and defined as ACO-BmTSP. This paper addresses the problem of solving the mTSP while considering several salesmen and keeping both the total travel cost at the minimum and the tours balanced. Eleven different problems with several variants were analyzed to validate the method. The 20 variants considered three to twenty salesmen regarding 11 to 783 cities. The results were compared with best-known solutions (BKSs) in the literature. Computational experiments showed that a total of eight final results were better than those of the BKSs, and the others were quite promising, showing that with few adaptations, it will be possible to obtain better results than those of the BKSs. Although the ACO metaheuristic does not guarantee that the best solution will be found, it is essential in problems with non-deterministic polynomial time complexity resolution or when used as an initial bound solution in an integer programming formulation. Computational experiments on a wide range of benchmark problems within an acceptable time limit showed that compared with four existing algorithms, the proposed algorithm presented better results for several problems than the other algorithms did.info:eu-repo/semantics/publishedVersio