Um modelo estocástico combinado de previsão sazonal para a precipitação no Brasil

Este artigo discute um modelo de previsão combinada para a realização de prognósticos climáticos na escala sazonal. Nele, previsões pontuais de modelos estocásticos são agregadas para obter as melhores projeções no tempo. Utilizam-se modelos estocásticos autoregressivos integrados a médias móveis, de suavização exponencial e previsões por análise de correlações canônicas. O controle de qualidade das previsões é feito através da análise dos resíduos e da avaliação do percentual de redução da variância não-explicada da modelagem combinada em relação às previsões dos modelos individuais. Exemplos da aplicação desses conceitos em modelos desenvolvidos no Instituto Nacional de Meteorologia (INMET) mostram bons resultados e ilustram que as previsões do modelo combinado, superam na maior parte dos casos a de cada modelo componente, quando comparadas aos dados observados.

Abordagem à previsão do preço de energia elétrica via métodos de suavização exponencial

Dissertação para a obtenção do grau de Mestre em Engenharia Electrotécnica Ramo de Energia

Previsão de curto prazo do consumo de energia elétrica

Dissertação para a obtenção do grau de Mestre em Engenharia Electrotécnica - Ramo de Energia

Metaheuristic approach to the Holt-Winters optimal short term load forecast

Electricity short-term load forecast is very important for the operation of power systems. In this work a classical exponential smoothing model, the Holt-Winters with double seasonality was used to test for accurate predictions applied to the Portuguese demand time series. Some metaheuristic algorithms for the optimal selection of the smoothing parameters of the Holt-Winters forecast function were used and the results after testing in the time series showed little differences among methods, so the use of the simple local search algorithms is recommended as they are easier to implement.

Metaheuristhic approach to the Holt-Winters optimal short term load forecast

Electricity short-term load forecast is very important for the operation of power systems. In this work a classical exponential smoothing model, the Holt-Winters with double seasonality was used to test for accurate predictions applied to the Portuguese demand time series. Some metaheuristic algorithms for the optimal selection of the smoothing parameters of the Holt-Winters forecast function were used and the results after testing in the time series showed little differences among methods, so the use of the simple local search algorithms is recommended as they are easier to implement.

Previsão de diagramas de carga via Holt-Winters com três sazonalidades

Dissertação para a obtenção do grau de Mestre em Engenharia Electrotécnica Ramo de Energia

Modelação e previsão de vendas no setor do retalho de calçado

Dissertação apresentada ao Instituto Politécnico do Porto para obtenção do Grau de Mestre em Logística Orientada por: Professora Doutora Patrícia Alexandra Gregório Ramos

Short-term Load Forecasting Based on Load Profiling

Load forecasting has gradually becoming a major field of research in electricity industry. Therefore, Load forecasting is extremely important for the electric sector under deregulated environment as it provides a useful support to the power system management. Accurate power load forecasting models are required to the operation and planning of a utility company, and they have received increasing attention from researches of this field study. Many mathematical methods have been developed for load forecasting. This work aims to develop and implement a load forecasting method for short-term load forecasting (STLF), based on Holt-Winters exponential smoothing and an artificial neural network (ANN). One of the main contributions of this paper is the application of Holt-Winters exponential smoothing approach to the forecasting problem and, as an evaluation of the past forecasting work, data mining techniques are also applied to short-term Load forecasting. Both ANN and Holt-Winters exponential smoothing approaches are compared and evaluated.

Desenvolvimento de um modelo para a gestão de stocks no grupo Nors

Hoje em dia, um dos grandes objetivos das empresas é conseguirem uma gestão eficiente. Em particular, empresas que lidam com grandes volumes de stocks têm a necessidade de otimizar as quantidades dos seus produtos armazenados, com o objetivo, de entre outros, reduzir os seus custos associados. O trabalho documentado descreve um novo modelo, desenvolvido para a gestão de encomendas de uma empresa líder em soluções de transporte. A eficiência do modelo foi alcançada com a utilização de vários métodos matemáticos de previsão. Salientam-se os métodos de Croston, Teunter e de Syntetos e Boylan adequados para artigos com procuras intermitentes e a utilização de métodos mais tradicionais, tais como médias móveis ou alisamento exponencial. Os conceitos de lead time, stock de segurança, ponto de encomenda e quantidade económica a encomendar foram explorados e serviram de suporte ao modelo desenvolvido. O stock de segurança recebeu especial atenção. Foi estabelecida uma nova fórmula de cálculo em conformidade com as necessidades reais da empresa. A eficiência do modelo foi testada com o acompanhamento da evolução do stock real. Para além de uma redução significativa do valor dos stocks armazenados, a viabilidade do modelo é reflectida pelo nível de serviço alcançado.

Creation of a web application for smart park system and parking prediction study for system integration

In the last few years, we have observed an exponential increasing of the information systems, and parking information is one more example of them. The needs of obtaining reliable and updated information of parking slots availability are very important in the goal of traffic reduction. Also parking slot prediction is a new topic that has already started to be applied. San Francisco in America and Santander in Spain are examples of such projects carried out to obtain this kind of information. The aim of this thesis is the study and evaluation of methodologies for parking slot prediction and the integration in a web application, where all kind of users will be able to know the current parking status and also future status according to parking model predictions. The source of the data is ancillary in this work but it needs to be understood anyway to understand the parking behaviour. Actually, there are many modelling techniques used for this purpose such as time series analysis, decision trees, neural networks and clustering. In this work, the author explains the best techniques at this work, analyzes the result and points out the advantages and disadvantages of each one. The model will learn the periodic and seasonal patterns of the parking status behaviour, and with this knowledge it can predict future status values given a date. The data used comes from the Smart Park Ontinyent and it is about parking occupancy status together with timestamps and it is stored in a database. After data acquisition, data analysis and pre-processing was needed for model implementations. The first test done was with the boosting ensemble classifier, employed over a set of decision trees, created with C5.0 algorithm from a set of training samples, to assign a prediction value to each object. In addition to the predictions, this work has got measurements error that indicates the reliability of the outcome predictions being correct. The second test was done using the function fitting seasonal exponential smoothing tbats model. Finally as the last test, it has been tried a model that is actually a combination of the previous two models, just to see the result of this combination. The results were quite good for all of them, having error averages of 6.2, 6.6 and 5.4 in vacancies predictions for the three models respectively. This means from a parking of 47 places a 10% average error in parking slot predictions. This result could be even better with longer data available. In order to make this kind of information visible and reachable from everyone having a device with internet connection, a web application was made for this purpose. Beside the data displaying, this application also offers different functions to improve the task of searching for parking. The new functions, apart from parking prediction, were: - Park distances from user location. It provides all the distances to user current location to the different parks in the city. - Geocoding. The service for matching a literal description or an address to a concrete location. - Geolocation. The service for positioning the user. - Parking list panel. This is not a service neither a function, is just a better visualization and better handling of the information.

Forecasting time series combining Holt-Winters and bootstrap approaches

Publicado em "AIP Conference Proceedings", Vol. 1648

Kysynnän ennustaminen leipomotuotteille ja ennusteen tarkkuuden arviointi

Tässä diplomityössä tutkittiin kysynnän ennustamista Vaasan & Vaasan Oy:n tuotteille. Ensin työssä perehdyttiin ennustamiseen ja sen tarjoamiin mahdollisuuksiin yrityksessä. Erityisesti kysynnän ennustamisesta saatavat hyödyt käytiin läpi. Kysynnän ennustamisesta haettiin ratkaisua erityisesti ongelmiin työvuorosuunnittelussa.Työssä perehdyttiin ennustemenetelmiin liittyvään kirjallisuuteen, jonka oppien perusteella tehtiin koe-ennustuksia yrityksen kysynnän historiadatan avulla. Koe-ennustuksia tehtiin kuudelle eri Turun leipomon koe-tuotteelle. Ennustettavana aikavälinä oli kahden viikon päiväkohtainen kysyntä. Tämän aikavälin erityisesti peruskysynnälle etsittiin ennustetarkkuudeltaan parasta kvantitatiivista ennustemenetelmää. Koe-ennustuksia tehtiin liukuvilla keskiarvoilla, klassisella aikasarja-analyysillä, eksponentiaalisen tasoituksen menetelmällä, Holtin lineaarisella eksponenttitasoituksen menetelmällä, Wintersin kausittaisella eksponentiaalisella tasoituksella, autoregressiivisillä malleilla, Box-Jenkinsin menetelmällä ja regressioanalyysillä. Myös neuroverkon opettamista historiadatalla ja käyttämistä ongelman ratkaisun apuna kokeiltiin.Koe-ennustuksien tulosten perusteella ennustemenetelmien toimintaa analysoitiin jatkokehitystä varten. Ennustetarkkuuden lisäksi arvioitiin mallin yksinkertaisuutta, helppokäyttöisyyttä ja sopivuutta yrityksen monien tuotteiden ennustamiseen. Myös kausivaihteluihin, trendeihin ja erikoispäiviin kiinnitettiin huomiota. Ennustetarkkuuden huomattiin parantuvan selvästi peruskysyntää ennustettaessa, jos ensin historiadata esikäsittelemällä puhdistettiin erikoispäivistä ja –viikoista.

Aplicación de algunos modelos de series temporales al mercado de la electricidad en España

Hoy día, todo el mundo tiene un ojo puesto en el Mercado Eléctrico en nuestro país. No existe duda alguna sobre la importancia que tiene el comportamiento de la demanda eléctrica. Una de las peculiaridades de la electricidad que producimos, es que hoy por hoy, no existen aún métodos lo suficientemente efectivos para almacenarla, al menos en grandes cantidades. Por consiguiente, la cantidad demandada y la ofertada/producida deben casar de manera casi perfecta. Debido a estas razones, es bastante interesante tratar de predecir el comportamiento futuro de la demanda, estudiando una posible tendencia y/o estacionalidad. Profundizando más en los datos históricos de las demandas; es relativamente sencillo descubrir la gran influencia que la temperatura ambiente, laboralidad o la actividad económica tienen sobre la respuesta de la demanda. Una vez teniendo todo esto claro, podemos decidir cuál es el mejor método para aplicarlo en este tipo de series temporales. Para este fin, los métodos de análisis más comunes han sido presentados y explicados, poniendo de relieve sus principales características, así como sus aplicaciones. Los métodos en los que se ha centrado este proyecto son en los modelos de alisado y medias móviles. Por último, se ha buscado una relación entre la demanda eléctrica peninsular y el precio final que pagamos por la luz.

Kysynnän ennakointiprosessin kehittäminen leipomoyrityksessä

Tämän työn tarkoituksena on kehittää lyhyen tähtäimen kysynnän ennakointiprosessia VAASAN Oy:ssä, jossa osa tuotteista valmistetaan kysyntäennakoiden perusteella. Valmistettavien tuotteiden luonteesta johtuva varastointimahdollisuuden puuttuminen, korkea toimitusvarmuustavoite sekä tarvittavien ennakoiden suuri määrä asettavat suuret haasteet kysynnän ennakointiprosessille. Työn teoriaosuudessa käsitellään kysynnän ennustamisen tarvetta, ennusteiden käyttökohteita sekä kysynnän ennustamismenetelmiä. Pelkällä kysynnän ennustamisella ei kuitenkaan päästä toimitusketjun kannalta optimaaliseen lopputulokseen, vaan siihen tarvitaan kokonaisvaltaista kysynnän hallintaa. Se on prosessi, jonka tavoitteena on tasapainottaa toimitusketjun kyvykkyydet ja asiakkaiden vaatimukset keskenään mahdollisimman tehokkaasti. Työssä tutkittiin yrityksessä kolmen kuukauden aikana eksponentiaalisen tasoituksen menetelmällä laadittuja ennakoita sekä ennakoijien tekemiä muutoksia niihin. Tutkimuksen perusteella optimaalinen eksponentiaalisen tasoituksen alfa-kerroin on 0,6. Ennakoijien tilastollisiin ennakoihin tekemät muutokset paransivat ennakoiden tarkkuutta ja ne olivat erityisen tehokkaita toimituspuutteiden minimoimisessa. Lisäksi työn tuloksena ennakoijien käyttöön saatiin monia päivittäisiä rutiineja helpottavia ja automatisoivia työkaluja.

Demand forecasting process design and methods in medium sized enterprise

The purpose of this thesis was to study the design of demand forecasting processes and management of demand. In literature review were different processes found and forecasting methods and techniques interviewed. Also role of bullwhip effect in supply chain was identified and how to manage it with information sharing operations. In the empirical part of study is at first described current situation and challenges in case company. After that will new way to handle demand introduced with target budget creation and how information sharing with 5 products and a few customers would bring benefits to company. Also the new S&OP process created within this study and organization for it.