Made available in DSpace on 2014-06-11T19:33:40Z (GMT). No. of bitstreams: 0 Previous issue date: 2011-01-06Bitstream added on 2014-06-13T19:44:37Z : No. of bitstreams: 1 ninin_mvl_dr_jabo.pdf: 504046 bytes, checksum: c4d5fb01770f50731225baa2e8790ca0 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Fundecitrus / O objetivo do presente trabalho foi construir modelos de favorabilidade para a expressão dos sintomas da mancha preta dos citros no estado de São Paulo e elaborar mapas de zonas de risco da expressão dos sintomas. Desenvolveu-se um sistema empírico com base em um banco de dados da ocorrência da doença e das condições climáticas, em campo, nos municípios de Barretos, Pedranópolis e Taquarituba, durante as Safras 2007/2008 e 2008/2009. A variedade de laranjeira doce utilizada nos experimentos foi a ‘Valência’ enxertada sobre limoeiro ‘Cravo’, com 10 anos de idade. Para a incidência da mancha preta foi avaliada a porcentagem de frutos com sintomas na planta e para a severidade, a porcentagem de casca lesionada por fruto. Na análise de regressão as variáveis climáticas e os dados de intensidade de doença foram selecionados no procedimento stepwise no programa SAS. Para a elaboração dos mapas de zonas de risco foram utilizados dados meteorológicos referentes as safras 2003/2004 e 2004/2005. Os modelos utilizados foram os construídos para o município de Pedranópolis que estava localizado na região 1 (Auriflama, Jales e Votuporanga), para o município de Barretos (região 2) e para o município de Taquarituba na região 3 (Tatuí e Campinas). A frequência dos dados foi horária e após a contabilização dos índices, foram calculadas as porcentagens de dias favoráveis à ocorrência das doenças durante o ano e em períodos pré-definidos. A partir destas informações, foram gerados os mapas temáticos do Estado de São Paulo, com a distribuição espacial da porcentagem de dias favoráveis à ocorrência das doenças. Os mapas de favorabilidade da mancha preta dos citros gerados com dados de campo apresentam a região Norte, central e Sudeste do estado de São Paulo com a maior porcentagem de dias favoráveis à expressão dos sintomas da doença... / The objective this study was to build forecasting models for the expression of citrus black spot in the state of Sao Paulo and produce maps of areas at risk of symptom expression. Developed an empirical system based on a database of disease occurrence and climatic conditions the city of Barretos, Pedranópolis and Taquarituba during the 2008/2009 and 2007/2008. The sweet orange variety was the 'Valencia', with 10 years. For the incidence of citrus black spot was evaluated the percentage of fruits with symptoms in the plant and the severity, the percentage of skin lesions fruit. In regression analysis climatic variables and data intensity of the disease were selected in stepwise in SAS. For maps of risk areas were used meteorological data regarding the 2004/2005 and 2003/2004. The models used were constructed for the city of Pedranópolis which was located in region 1 (Auriflama, Jales and Votuporanga), for the city of Barretos (region 2) and the region 3 Taquarituba (Tatuí and Campinas). The frequency of hourly and after accounting for the indices, we calculated the percentage of days favorable to the occurrence of disease. From this information, thematic maps were generated in the state of São Paulo, with the spatial distribution of the percentage of days favorable to the occurrence of diseases. Favorability maps of citrus black spot generated with field data show the north, central and southeastern state of Sao Paulo with the largest percentage of days favorable to the expression of disease symptoms. All citrus region of São Paulo is in favorable climatic conditions the expression of the symptoms of citrus black spot

Resumo: O objetivo do presente trabalho foi construir modelos de favorabilidade para a expressão dos sintomas da mancha preta dos citros no estado de São Paulo e elaborar mapas de zonas de risco da expressão dos sintomas. Desenvolveu-se um sistema empírico com base em um banco de dados da ocorrência da doença e das condições climáticas, em campo, nos municípios de Barretos, Pedranópolis e Taquarituba, durante as Safras 2007/2008 e 2008/2009. A variedade de laranjeira doce utilizada nos experimentos foi a 'Valência' enxertada sobre limoeiro 'Cravo', com 10 anos de idade. Para a incidência da mancha preta foi avaliada a porcentagem de frutos com sintomas na planta e para a severidade, a porcentagem de casca lesionada por fruto. Na análise de regressão as variáveis climáticas e os dados de intensidade de doença foram selecionados no procedimento stepwise no programa SAS. Para a elaboração dos mapas de zonas de risco foram utilizados dados meteorológicos referentes as safras 2003/2004 e 2004/2005. Os modelos utilizados foram os construídos para o município de Pedranópolis que estava localizado na região 1 (Auriflama, Jales e Votuporanga), para o município de Barretos (região 2) e para o município de Taquarituba na região 3 (Tatuí e Campinas). A frequência dos dados foi horária e após a contabilização dos índices, foram calculadas as porcentagens de dias favoráveis à ocorrência das doenças durante o ano e em períodos pré-definidos. A partir destas informações, foram gerados os mapas temáticos do Estado de São Paulo, com a distribuição espacial da porcentagem de dias favoráveis à ocorrência das doenças. Os mapas de favorabilidade da mancha preta dos citros gerados com dados de campo apresentam a região Norte, central e Sudeste do estado de São Paulo com a maior porcentagem de dias favoráveis à expressão dos sintomas da doença... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: The objective this study was to build forecasting models for the expression of citrus black spot in the state of Sao Paulo and produce maps of areas at risk of symptom expression. Developed an empirical system based on a database of disease occurrence and climatic conditions the city of Barretos, Pedranópolis and Taquarituba during the 2008/2009 and 2007/2008. The sweet orange variety was the 'Valencia', with 10 years. For the incidence of citrus black spot was evaluated the percentage of fruits with symptoms in the plant and the severity, the percentage of skin lesions fruit. In regression analysis climatic variables and data intensity of the disease were selected in stepwise in SAS. For maps of risk areas were used meteorological data regarding the 2004/2005 and 2003/2004. The models used were constructed for the city of Pedranópolis which was located in region 1 (Auriflama, Jales and Votuporanga), for the city of Barretos (region 2) and the region 3 Taquarituba (Tatuí and Campinas). The frequency of hourly and after accounting for the indices, we calculated the percentage of days favorable to the occurrence of disease. From this information, thematic maps were generated in the state of São Paulo, with the spatial distribution of the percentage of days favorable to the occurrence of diseases. Favorability maps of citrus black spot generated with field data show the north, central and southeastern state of Sao Paulo with the largest percentage of days favorable to the expression of disease symptoms. All citrus region of São Paulo is in favorable climatic conditions the expression of the symptoms of citrus black spot / Orientador: Modesto Barreto / Coorientador: Marcel Bellato Spósito / Banca: Érika Auxiliadora Giacheto Scaloppi / Banca: Fernando Alves Azevedo / Banca: José Carlos Barbosa / Banca: Rita de Cássia Panizzi / Doutor

This thesis has been performed at IKEA Jönköping. The purpose was to identify what kind of forecasting system IKEA Jönköping is using and analyze its problems. The data collection was based on interviews with a total of 6 people working at IKEA Jönköping, IKEA of Sweden (IOS) in Älmhult and observation at the Sales Supply Support division (SSS).  From the empirical study several problems were identified linked with the performance of the forecasting. Problems with understanding the initial forecast from IOS were identified and this was because of lack of information about demand. SSS also wanted to know their local market in a better way this to be able to make more accurate forecast. Finally all the departments at IKEA Jönköping which were working with forecasting wanted a closer collaboration between SSS, sales and the logistics department also wanted to have better information exchange. The result from this thesis explain what kind of forecasting system IKEA Jönköping is using and gives suggestions to solve the problems mentioned above. We have highlighted the importance of having a closer collaboration between IOS and IKEA Jönköping and between the different departments working with forecasting. Furthermore we have explained the importance of creating guidelines and routines regarding the forecasts and the flow of information. By considering our solutions presented in this thesis we think that the problems addressed above could be managed and hopefully lead towards a better forecasting performance at IKEA Jönköping.

Orientador: Luiz Henrique Antunes Rodrigues / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Agricola / Made available in DSpace on 2018-08-11T10:02:19Z (GMT). No. of bitstreams: 1 Meira_CarlosAlbertoAlves_D.pdf: 2588338 bytes, checksum: 869cc28d2c71dbc901870285cc32d8f9 (MD5) Previous issue date: 2008 / Resumo: Sistemas de alerta de doenças de plantas permitem racionalizar o uso de agrotóxicos, mas são pouco utilizados na prática. Complexidade dos modelos, dificuldade de obtenção dos dados necessários e custos para o agricultor estão entre as razões que inibem o seu uso. Entretanto, o desenvolvimento tecnológico recente - estações meteoro lógicas automáticas, bancos de dados, monitoramento agrometeorológico na Web e técnicas avançadas de análise de dados - permite se pensar em um sistema de acesso simples e gratuito. Uma instância do processo de descoberta de conhecimento em bases de dados foi realizada com o objetivo de avaliar o uso de classificação e de indução de árvores de decisão na análise e no alerta da ferrugem do cafeeiro causada por Hemileia vastatrix. Taxas de infecção calculadas a partir de avaliações mensais de incidência da ferrugem foram agrupadas em três classes: TXl - redução ou estagnação; TX2 - crescimento moderado (até 5 p.p.); e TX3 - crescimento acelerado (acima de 5 p.p.). Dados meteorológicos, carga pendente de frutos do cafeeiro (Coffea arabica) e espaçamento entre plantas foram as variáveis independentes. O conjunto de treinamento totalizou 364 exemplos, preparados a partir de dados coletados em lavouras de café em produção, de outubro de 1998 a outubro de 2006. Uma árvore de decisão foi desenvolvida para analisar a epidemia da ferrugem do cafeeiro. Ela demonstrou seu potencial como modelo simbólico e interpretável, permitindo a identificação das fronteiras de decisão e da lógica contidas nos dados, allf'iliando na compreensão de quais variáveis e como as interações dessas variáveis condicionaram o progresso da doença no campo. As variáveis explicativas mais importantes foram a temperatura média nos períodos de molhamento foliar, a carga pendente de frutos, a média das temperaturas máximas diárias no período de inG:!Jbação e a umidade relativa do ar. Os modelos de alerta foram deserivolvtdos considerando taxas de infecção binárias, segundo os limites de 5 p.p e 10 p.p. (classe- '1' para taxas maiores ou iguais ao limite; classe 'O', caso contrário). Os modelos são específicos para lavouras com alta carga pendente ou para lavouras com baixa carga. Os primeiros tiveram melhor desempenho na avaliação. A estimativa de acurácia, por validação cruzada, foi de até 83%, considerando o alerta a partir de 5 p.p. Houve ainda equilíbrio entre a acurácia e medidas importantes como sensitividade, especificidade e confiabilidade positiva ou negativa. Considerando o alerta a partir de 10 p.p., a acurácia foi de 79%. Para lavouras com baixa carga pendente, os modelos considerando o alerta a partir de 5 p.p. tiveram acurácia de até 72%. Os modelos para a taxa de infecção mais elevada (a partir de 10 p.p.) tiveram desempenho fraco. Os modelos mais bem avaliados mostraram ter potencial para servir como apoio na tomada de decisão referente à adoção de medidas de controle da ferrugem do cafeeiro. O processo de descoberta de conhecimento em bases de dados foi caracterizado, com a intenção de que possa vir a ser útil em aplicações semelhantes para outras culturas agrícolas ou para a própria cultura do café, no caso de outras doenças ou pragas / Abstract: Plant disease warning systems can contribute for diminishing the use of chemicals in agriculture, but they have received limited acceptance in practice. Complexity of models, difficulties in obtaining the required data and costs for the growers are among the reasons that inhibit their use. However, recent technological advance - automatic weather stations, databases, Web based agrometeorological monitoring and advanced techniques of data analysis - allows the development of a system with simple and free access. A process .instance of knowledge discovery in databases has been realized to evaluate the use of classification and decision tree induction in the analysis and warning of coffee rust caused by Hemileia vastatrix. Infection rates calculated from monthly assessments of rust incidence were grouped into three classes: TXl - reduction or stagnation; TX2 - moderate growth (up to 5 pp); and TX3 - accelerated growth (above 5 pp). Meteorological data, expected yield and space between plants were used as independent variables. The training data set contained 364 examples prepared from data collected in coffee-growing areas between October 1998 and October 2006. A decision tree has been developed to analyse the coffee rust epidemics. The decision tree demonstrated its potential as a symbolic and interpretable model. Its mo deI representation identified the existing decision boundaries in the data and the logic underlying them, helping to understand which variables, and interactions between these variables, led to, coffee rust epidemics in the field. The most important explanatory variables were mean temperature during leaf wetness periods, expected yield, mean of maximum temperatures during the incubation period and relative air humidity. The warning models have been developed considering binary infection rates, according to the 5 pp and 10 pp thresholds, (class '1' for rates greater than or equal the threshold; class 'O;, otherwise). These models are specific for growing are as with high expected yield or areas with low expected yield. The former had best performance in the evaluation. The estimated accuracy by cross-validation was up to 83%, considering the waming for 5 pp and higher. There was yet equivalence between accuracy and such important measures like sensitivity, specificity a~d positive or negative reliability. Considering the waming for 10 pp and higher, the accuracy was 79%. For growing areas with low expected yield, the accuracy of the models considering the waming for 5 pp and higher was up to 72%. The models for the higher infection rate (10 pp and higher) had low performance. The best evaluated models showed potential to be used in decision making about coffee rust disease control. The process of knowledge discovery in databases was characterized in such a way it can be employed in similar problems of the application domain with other crops or other coffee diseases or pests / Doutorado / Planejamento e Desenvolvimento Rural Sustentável / Doutor em Engenharia Agrícola

The development of the Severe Weather Impact Forecasting System (SWIFS) for flash flood hazards in South Africa is described in this thesis. Impact forecasting addresses the need to move from forecasting weather conditions to forecasting the consequential impact of these conditions on people and their livelihoods. SWIFS aims to guide disaster managers to take early action to minimise the adverse effects of flash floods focussing on hotspots where the largest impact is expected. The first component of SWIFS produced an 18-hour probabilistic outlook of potential occurrence of flash floods. This required the development of an ensemble forecast system of rainfall for small river basins (the forecasting model component), based on the rainfall forecast of a deterministic numerical weather prediction model, to provide an 18-hour lead-time, taking into account forecast uncertainty. The second component of SWIFS covered the event specific societal and structural impacts of these potential flash floods, based on the interaction of the potential occurrence of flash floods with the generalised vulnerability to flash floods of the affected region (the impact model component). The impact model required an investigation into the concepts of regional vulnerability to flash floods, and the development of relevant descriptive and mathematical definitions in the context of impact forecasting. The definition developed in the study links impact forecasting to the likelihood and magnitude of adverse impacts to communities under threat, based on their vulnerability and due to an imminent severe weather hazard. Case studies provided evidence that the concept of SWIFS can produce useful information to disaster managers to identify areas most likely to be adversely affected in advance of a hazardous event and to decide on appropriate distribution of their resources between the various hotspots where the largest impacts would be. SWIFS contributes to the current international research on short-term impact forecasting by focussing on forecasting the impacts of flash floods in a developing country with its limited spatial vulnerability information. It provides user-oriented information in support of disaster manager decision-making through additional lead-time of the potential of flash floods, and the likely impact of the flooding. The study provides a firm basis for future enhancement of SWIFS to other severe weather hazards in South Africa. / Thesis (PhD)--University of Pretoria, 2015. / gm2015 / Geography, Geoinformatics and Meteorology / PhD / Unrestricted

Lhumectation des surfaces végétales, due principalement aux précipitations sous forme de pluie ou de rosée, joue un rôle déterminant lors de la phase de contamination des plantes par de nombreux agents phytopathogènes. La connaissance de la pluie et de la rosée constitue un élément fondamental pour létude et la compréhension du fonctionnement des modèles de simulation des épidémies et des systèmes d'avertissements agricoles. Lobjectif de cette recherche est de contribuer à lamélioration du système davertissement des principales maladies cryptogamiques affectant le blé dhiver au sud de Belgique et au G-D de Luxembourg. Notre démarche a consisté, dans un premier temps à évaluer les potentialités du radar météorologique de Wideumont. Nous avons décrit son fonctionnement général ainsi que son principe de mesure et nous avons détaillé les différentes sources derreur qui affectent les estimations de précipitations dérivées des observations radar. Les mesures radar sont moins précises que les mesures de précipitations par des pluviomètres. Néanmoins, le radar permet dobserver en temps réel les précipitations sur un large domaine avec une très bonne résolution spatiale et temporelle. La comparaison quantitative et qualitative des précipitations mesurées au sol avec celles estimées par le radar a été faite sur une période de trois ans (2003, 2004 et 2005). Les résultats de la validation des cumuls mensuels font apparaître que le radar a tendance à sous-estimer les précipitations. Lerreur calculée pour lensemble des stations varie entre -50% et +12%. La validation qualitative du radar a été réalisée sur des occurrences de cumuls horaires. Les indices calculés à partir des tables de contingence donnent des valeurs de POD (Probability Of Detection) entre 0.44 et 0.80 durant la période étudiée. Limpact des estimations radar sur les périodes dinfection de Septoria tritici simulées par PROCULTURE a été évalué durant trois saisons culturales (2003, 2004 et 2005) par comparaison entre les données de sortie du modèle (alimenté par des estimations radar de précipitations horaires) et les estimations visuelles du développement des symptômes de la maladie sur les trois dernières feuilles. Les outputs de PROCULTURE via les données radar ont montré un grand accord entre la simulation et lobservation. Le radar météorologique devrait dès lors être bénéfique pour des régions où le réseau des pluviomètres est inexistant (ou moins dense) et où lincidence de la septoriose est importante. Dans un deuxième temps, sur base dune recherche bibliographique, un modèle dhumectation a été choisi. Le modèle sélectionné, appelé SWEB, se base sur le bilan énergétique et le bilan hydrique. Il simule la durée dhumectation due à la pluie et à la rosée sur lensemble du couvert végétal à partir des données issues des stations agrométéorologiques. Le modèle a été ensuite testé et validé sur différentes variétés de blé dhiver. Les données de sortie du modèle ont été comparées statistiquement aux mesures des capteurs (préalablement calibrés) et aux données dobservation obtenues sur des parcelles expérimentales et au champ durant les saisons culturales 2006 et 2007. Sur base des résultats obtenus, le modèle SWEB semble sous-estimer la durée dhumectation et plus particulièrement pour les événements de la fin dhumectation (dryoff). Lerreur moyenne en général est inférieure à 90 minutes. Dans un troisième temps, afin dobtenir une relation entre les périodes dhumectation et le développement de la septoriose sur les trois dernières feuilles, les périodes dhumectation simulées par SWEB ont été comparées dune part aux périodes dinfection de Septoria tritici simulées par PROCULTURE et dautre part aux estimations visuelles. Le modèle de la durée dhumectation simule avec succès des périodes dhumectations, dues à la fois à la rosée et à la pluie, qui ont déclenché linfection de la septoriose observée sur des parcelles expérimentales. Une durée minimale dhumectation favorable à linfection des feuilles de blé par Septoria tritici a été déterminée. Il est donc désormais nécessaire délaborer un système opérationnel intégrant le radar météorologique, le modèle de la durée dhumectation et le modèle épidémiologique. Notre travail a permis dacquérir via lanalyse des données agrométéorologiques et des données phytopathologiques, les connaissances nécessaires à lélaboration dun tel système et de participer ainsi à lamélioration des modèles davertissements existants. En effet, nous avons analysé les avantages et les limites du système radar comme données dentrée aux modèles et son aptitude dans la spatialisation des données. Nous avons également testé le modèle dhumectation pour la détermination des périodes dinfection nécessaires au développement de la septoriose. Dans une perspective dune meilleure opérationnalisation du système, lapproche envisagée pourrait facilement être intégrée dans le système existant pour la simulation dautres maladies comme les rouilles, loïdium et la fusariose à léchelle régionale. En définitive, ce travail aura prouvé une fois de plus lintérêt du "mariage" entre lagrométéorologie et la phytopathologie. [en] Summary - Weather-Radar Rainfall Measurement and Simulated Surface Wetness Duration for Septoria Leaf Blotch Risk Assessment. The persistence of free moisture on leaves, mainly as a result of precipitation in the form of rainfall or dew, plays a major role during the process of plant infection by most fungal pathogens. Acquiring rainfall and leaf moisture information is needed for accurate and reliable disease prediction and management. The objective of this research is to contribute to improve forecasting Septoria leaf blotch and other fungal pathogens on winter wheat in Belgium and Luxembourg./In the first part of this work, the potential of weather-radar rainfall estimates for plant disease forecasting is discussed. At first step, we focused on assessing the accuracy and limitations of radar-derived precipitation estimates, compared with rain-gauge data. In a second step, the Septoria leaf blotch prediction model PROCULTURE was used to assess the impact on the simulated infection rate of using, as input data, rainfall estimated by radar instead of rain gauge measurements. When comparing infection events simulated by PROCULTURE using radar-derived estimates and reference rain gauge measurements, the probability of detection (POD) of infection events was high (0.83 on average), and the false alarm ratio (FAR) of infection events was not negligible (0.24 on average). FAR decreased to 0 and POD increased (0.85 on average) for most stations, when the model outputs for both datasets were compared against visual observations of Septoria leaf blotch symptoms. Analysis of 148 infection events observed over three years at four locations showed no significant difference in the number of simulated infection events using either radar assessments or gauge measurements. This suggests that, for a given location, radar estimates are just as reliable for predicting infection events as rain gauges. As radar is able to estimate rainfall occurrence over a continuous space, unlike weather station networks that do observations at only a limited number of points, it has the great advantage of being able to predict the risk of infection at each point within an area of interest with an accuracy equivalent to rain gauge observations. This gives radar an important advantage that could significantly improve existing warning systems. In the second part, a physical model based on the energy balance, known as the Surface Wetness Energy Balance (SWEB), was applied for the simulation of Surface Wetness Duration (SWD) on winter wheat canopy. The model, developed in the United States on grapes canopies, was adapted for the winter wheat cultivars and was applied for use with agrometeorological data easily available from standard weather stations and weather-radar rainfall estimates. The SWEB model simulates surface wetness duration for both dew and rain events. The model was validated with data measured by sensors and with visual observations of SWD conducted in experimental plots during two cropping seasons in 2006 and 2007. The wetness was observed visually by assessing the presence or absence of surface water on leaves. Based on the results, the SWEB model appeared to underestimate surface wetness duration and especially for the dry-off events when compared statistically to visual observations. The error, on average, is generally less than 90 minutes. In order to establish a relationship between the surface wetness periods and Septoria leaf blotch development risk on the top three leaves, the SWEB model SWD outputs were compared with the number of hours of high probability of infection simulated by PROCULTURE as well as with visual plant diseases observations. A minimal surface wetness duration of favourable infection conditions for Septoria tritici was established. It is now required to develop an operational system that would integrate weather radar, surface wetness duration and foliar epidemic model. In this work, we have analyzed the advantages and limitations of the radar system as input to models and its ability for spatial interpolation of rainfall. We also tested the model for the determination of surface wetness periods required for Septoria Leaf Blotch Risk development. The proposed approach could be integrated in the existing system. Finally this approach shows once more the "happy marriage" between agrometeorology and plant disease management.

With the aim for higher economical efficiency, considerable and radical changes have occurred in the worldwide electricity sector since the beginning of 1980s. By that time, the electricity sector has been controlled by the state-owned vertically integrated monopolies which manage and control all generation, transmission, distribution and retail activities and the consumers buy electricity with a price set by these monopolies in that system. After the liberalization and restructuring of the electricity power sector, separation and privatization of these activities have been widely seen. The main purpose is to ensure competition in the market where suppliers and consumers compete with each other to sell or buy electricity from the market and the consumers buy the electricity with a price which is based on competition and determined according to sell and purchase bids given by producers and customers rather than a price set by the government. Due to increasing competition in the electricity market, accurate electricity price forecasts have become a very vital need for all market participants. Accurate forecast of electricity price can help suppliers to derive their bidding strategy and optimally design their bilateral agreements in order to maximize their profits and hedge against risks. Consumers need accurate price forecasts for deriving their electricity usage and bidding strategy for minimizing their utilization costs. This thesis presents the determination of system day ahead price (SGOF) at the day ahead market and system marginal price (SMF) at the balancing power market in detail and develops artificial neural network models together with multiple linear regression models to forecast these electricity prices in Turkish electricity market. Also the methods used for price forecasting in the literature are discussed and the comparisons between these methods are presented. A series of historical data from Turkish electricity market is used to understand the characteristics of the market and the necessary input factors which influence the electricity price is determined for creating ANN models for price forecasting in this market. Since the factors influencing SGOF and SMF are different, different ANN models are developed for forecasting these prices. For SGOF forecasting, historical price and load values are enough for accurate forecasting, however, for SMF forecasting the net instruction volume occurred due to real time system imbalances is needed in order to increase the forecasting accuracy.