Short Term Load Forecasting New Year Celebration Holiday Using Interval Type-2 Fuzzy Inference System (Case Study: Java – Bali Electrical System)

Celebration of New Year In the Indonesian is constituted the one of the visit Indonesian’s tourism. This event course changes the load of electrical energy. The electrical energy providers that control and operation of electrical in Java and Bali (Java, Bali Electrical System) is required to be able to ensure continuity of load demand at this time, and forecast for the future. Short-term load forecasting very need to be supported by computational methods for simulation and validation. The one of computation’s methods is Interval Type – 2 Fuzzy Inference System (IT-2 FIS). Interval Type-2 Fuzzy Inference System (IT-2 FIS) as the development of methods of Interval Type-1 Fuzzy Inference System (IT-1 FIS), it is appropriate to be used in load forecasting because it has the advantages that very flexible on the change of the footprint of uncertainty (FOU), so it supports to establish an initial processing of the time series, computing, simulation and validation of system models. Forecasting methods used in this research are IT-2 FIS. The process for to know and analyzing the peak load a day is the specially day and 4 days before New year Celebration in the previous year continued analysis by using IT-2 FIS will be obtained at the peak load forecasting New Year Celebration in the coming year. This research shown the average of error value in 2012, 2013 and 2014 is 0,642%. This value is better than using the IT-1 FIS which has a value of error to 0.649%. This research concluded that IT-2 FIS can be used in Short Term Load Forecasting

Forecasting Unemployment Rate Using a Neural Network with Fuzzy Inference System

Greece is a low-productivity economy with an ineffective welfare state, relying almost exclusively on low wages and social transfers. Failure to come to terms with this reality hampers both the appropriateness of EU recommendations and the Greek government's capacity to deal with unemployment. Rather than finding a job in a family business or through relationship contacts, young people stay unemployed. Nor can people move back to their village of origin so easily. The underground economy, and the mass of small companies which characterize the Greek economy are booming, on paper. One in three members of the workforce are "self-employed", compared to one in seven in the EU as a whole. (International Viewpoint) An unemployed person in Greece is 2,15 times more likely to suffer poverty than a person in employment. Yet in Greece there are perhaps even more influential factors in determining increased risk of poverty. Thus while unemployment is a crucial factor in the risk of poverty, it is neither the only nor the most significant factor. The paper presents a new technique in the field of unemployment modeling in order to forecast unemployment index. Techniques from the Artificial Neural Networks and from fuzzy logic have been combined to generate a neuro-fuzzy model. The input is a time series. Classical statistics measures are calculated in order to asses the model performance. Further the results are compared with an ARMA and an AR model.forecasting, neural network, unemployment

Lean Forecasting In Software Projects

Quando se desenvolve um projeto de software, é reconhecível que estimativas precisas do esforço envolvido no desenvolvimento são uma parte importante na gestão bem-sucedida do projeto. Embora este processo seja tão importante, desenvolvedores e especialistas não conseguem normalmente estimar precisamente o esforço, tempo e custo que o projeto a ser desenvolvido terá. Isto é inerente à incerteza subjacente à sua atividade. Depois da primeira estimativa do esforço ser feita, o projeto pode, com alguma probabilidade, necessitar de se adaptar a circunstâncias em evolução, o que pode levar a mudanças nas características do projeto, e subsequentemente levar a que os gestores ponham mais pressão nos desenvolvedores para que sejam respeitados os prazos de entrega. No fim, o desenvolvimento do projeto irá, provavelmente, atrasar-se e estes atrasos não só afetam a equipa de desenvolvimento, mas também outras partes da empresa, como os departamentos responsaveis pelos funcionários e pelo marketing. Isto pode, em algumas situações, levar a que a empresa perca tempo e muitas vezes a confiança do cliente interessado no projeto. Mesmo que a estimativa seja precisa o suficiente para que as datas de entrega sejam respeitadas, métodos que dependem das estimativas de humanos consomem, normalmente, muito tempo, o que pode representar um problema quando equipas gastam tempo precioso a fazer estimativas. De maneira a mitigar estes problemas, iremos procurar identificar as motivações e forças em jogo no processo de fazer estimativas precisas e determinar que métodos de previsão alcançam os resultados mais precisos com alguma generalização, de modo a satisfazer a variedade de projetos de software existente. Vamos nos focar nos métodos de previsão devido à sua automaticidade, que irá ajudar a reduzir o tempo que as equipas gastam em estimações, mantendo a precisão dos resultados. Este método deve, também, ser fácil de perceber, implementar e usar, logo o número de dados que deve receber e a sua dificuldade de obter deve ser reduzida. As previsões do método devem conter um certo nível de ambiguidade, de modo a representar melhor o problema. Para a fase de validação do método, uma ferramenta baseada no método irá ser desenvolvida, testada em termos de eficácia e precisão contra outros métodos existentes, e irá ser integrada com ferramentas de gest�\xA3o de desenvolvimento de software, de modo a validar a sua usabilidade em projetos reais durante a fase de desenvolvimento destes. Assim, o objetivo principal desta dissertação é o de ajudar a reduzir o tempo perdido em estimações, mantendo ou até melhorando a precisão das previsões feitas e mantendo a facilidade de percepção e de uso para os desenvolvedores e equipas que utilizem este método.When developing a software project, it's recognisable that accurate estimations of development effort play an important part in the successful management of the project. Although this process is so important, developers and experts can't usually estimate accurately the effort, time and cost of a project to be developed. This is inherit to the uncertainty that underlies their activity. After the first estimation of the effort, the project may, with some likelihood, need to adapt to evolving circumstances, which may lead to changes in its scope, and consequently lead to managers putting pressure in the developers to respect delivery dates. In the end, the project's development will, probably, get delayed and this delays not only affect the development team but also other parts of the company, such as staffing or marketing. This could, in some situations, lead to the company losing time and in many times the trust of the stakeholder. Even if the estimate is accurate enough so that delivery dates are respected, methods that relay on Human estimation are, often, time consuming, what can represent a problem when teams waste precious time in making estimations. In order to mitigate this problems, we will seek to identify the motivations and forces playing in a accurate estimate and determine which forecast method could provide the bet- ter accuracy with some generalization, in order to satisfy the existing variety of software projects. We will focus on forecast methods because of their automatability, that will help reduce the time teams waste on estimations, still delivering accurate results. This method must also be easy to understand, implement and use, so the number of inputs required and the difficulty to collect this inputs should be low. The output of the method should contain a certain level of uncertainty, in order to better represent the problem. In order to validate this method, a tool based on it will be developed, tested in terms of effective- ness and accuracy against other existing methods, and it will be integrated with software development management tools to validate it's ability to be used in real projects during their development phase. Following this lines, the main goal of this dissertation is to help reduce the time wasted in estimations, while maintaining or even increase the accuracy of the prediction made and maintaining the understandability and usability easy for the teams and developers using it

The Application of Artificial Intelligence in Project Management Research: A Review

The field of artificial intelligence is currently experiencing relentless growth, with innumerable models emerging in the research and development phases across various fields, including science, finance, and engineering. In this work, the authors review a large number of learning techniques aimed at project management. The analysis is largely focused on hybrid systems, which present computational models of blended learning techniques. At present, these models are at a very early stage and major efforts in terms of development is required within the scientific community. In addition, we provide a classification of all the areas within project management and the learning techniques that are used in each, presenting a brief study of the different artificial intelligence techniques used today and the areas of project management in which agents are being applied. This work should serve as a starting point for researchers who wish to work in the exciting world of artificial intelligence in relation to project leadership and management

Design and Modeling of Stock Market Forecasting Using Hybrid Optimization Techniques

In this paper, an artificial neural network-based stock market prediction model was developed. Today, a lot of individuals are making predictions about the direction of the bond, currency, equity, and stock markets. Forecasting fluctuations in stock market values is quite difficult for businesspeople and industries. Forecasting future value changes on the stock markets is exceedingly difficult since there are so many different economic, political, and psychological factors at play. Stock market forecasting is also a difficult endeavour since it depends on so many various known and unknown variables. There are several ways used to try to anticipate the share price, including technical analysis, fundamental analysis, time series analysis, and statistical analysis; however, none of these approaches has been shown to be a consistently reliable prediction tool. We built three alternative Adaptive Neuro-Fuzzy Inference System (ANFIS) models to compare the outcomes. The average of the tuned models is used to create an ensemble model. Although comparable applications have been attempted in the literature, the data set is extremely difficult to work with because it only contains sharp peaks and falls with no seasonality. In this study, fuzzy c-means clustering, subtractive clustering, and grid partitioning are all used. The experiments we ran were designed to assess the effectiveness of various construction techniques used to our ANFIS models. When evaluating the outcomes, the metrics of R-squared and mean standard error are mostly taken into consideration. In the experiments, R-squared values of over.90 are attained

Predicting Completion Time for Production Line in a Supply Chain System through Artificial Neural Networks

Completion time in manufacturing sector is the time needed to produce a product through production processes in sequence and it reflects the delivery performance of such company in supply chain system to meet customer demands on time. However, actual completion time always deviated from the standard completion time due to unavoidable factors and consequently affect delivery due date and ultimately lead to customer dissatisfaction. Therefore, this paper predicts completion time based on historical data of production line activities and discovers the most influential factor that contributes to the tardiness or a late jobs due date from its completion time. A well-known company in producing audio speaker is selected as a case company. Based on the review of previous works, it is found that Artificial Neural Networks (ANN) has superior capability in prediction of future occurrence by capturing the underlying relationship among variables through historical data. Besides, ANN is also capable to provide final weight for each of related variable. Variable with the highest value of final weight indicates the most influential variable and should be concerned more to solve completion time issue which has persisted among entities in supply chain system. The obtained result is expected to become an advantageous guidance for every entity in supply chain system to fulfil completion time requirement as requested by customer in order to survive in this turbulent market place

Strategic Unification of Artificial Intelligence in Foreign Direct Investment Application Forms

A foreign direct investment (FDI) is a very popular method of investing overseas but different from a stock investment in a foreign company. It could be purchasing of an interest in a company by an investor located outside its borders and in most cases, governments pay special interest on them. This is a business decision to acquire a substantial stake in a foreign business or to buy it outright as to expand its operations to a new region. Embedding artificial intelligence (AI) across the business requires significant investment and a change in overall approach. It is highly constructive and productive transformation that should be planned professionally, applied systematically, and managed strategically. AI drives meaningful value to business through better decision-making and consumer-facing applications. The general perception about filling a FDI application is a cumbersome job. Some countries manage this stage very methodically and investors always give priority for them as they can commence the production/business activities within a short period. Those countries who fail to gain this competitive advantage tend to lose the FDI opportunities even if they own various other advantages of resources to attract investors. This paper attempts to evaluate the potential of embedding a strategic unification of artificial intelligence in the application forms used to fill by investors at the time of starting foreign direct investment projects

Composite Monte Carlo Decision Making under High Uncertainty of Novel Coronavirus Epidemic Using Hybridized Deep Learning and Fuzzy Rule Induction

In the advent of the novel coronavirus epidemic since December 2019, governments and authorities have been struggling to make critical decisions under high uncertainty at their best efforts. Composite Monte-Carlo (CMC) simulation is a forecasting method which extrapolates available data which are broken down from multiple correlated/casual micro-data sources into many possible future outcomes by drawing random samples from some probability distributions. For instance, the overall trend and propagation of the infested cases in China are influenced by the temporal-spatial data of the nearby cities around the Wuhan city (where the virus is originated from), in terms of the population density, travel mobility, medical resources such as hospital beds and the timeliness of quarantine control in each city etc. Hence a CMC is reliable only up to the closeness of the underlying statistical distribution of a CMC, that is supposed to represent the behaviour of the future events, and the correctness of the composite data relationships. In this paper, a case study of using CMC that is enhanced by deep learning network and fuzzy rule induction for gaining better stochastic insights about the epidemic development is experimented. Instead of applying simplistic and uniform assumptions for a MC which is a common practice, a deep learning-based CMC is used in conjunction of fuzzy rule induction techniques. As a result, decision makers are benefited from a better fitted MC outputs complemented by min-max rules that foretell about the extreme ranges of future possibilities with respect to the epidemic.Comment: 19 page