Economy in China has been blooming recently. The business potential has drawn attention from all over the world. Now varied industries have started to locate in China. Furthermore, it is believed that the interaction between cross-strait will be very close with the condition that both China and Taiwan have joined in WTO. This research will be focused on 1.The impact and the influence on higher education publishers in Taiwan after both China and Taiwan joined in WTO. 2. Is it a threat or an opportunity to the publishers with the consequent implementation from WTO? 3. What is the role of foreign publishers in Taiwan under this circumstance? As publishing industry in China hasn¡¦t been opened yet, this research will target on the discussion of co-publishing model from cross-strait. Taking a case study and its analysis, it is found that publishing industry has been identified as traditional industry but its upgrade and transition can not be applied to traditional industry. Therefore, the conclusions are as follow: 1. The regulations of publishing in China are still the key factors. 2. The extent of the resource publishers possess is the key factor of entering higher education market in China. 3. Publishers in Taiwan are more flexible to the consequent implementation. 4. Core competence is not equivalent to regional competitive advantage. 5. Market in China will be another opportunity for Taiwan. 6. The position of publishers in Taiwan in Chinese publishing has been changed. 7. Taiwan is the bridge between China and the rest of the world. 8. Each region has its own individual business model. 9. There is a remarkable difference of the process between publishing industry and traditional industry.

En 2010, ERDF (Electricité Réseau Distribution France) a entamé la mise en place du projet « Linky » dont l'objectif est d'installer 35 millions de compteurs intelligents en France. Ces compteurs permettront de collecter les données de consommation en « temps réel », avec lesquelles des modèles de charge plus précis pourront être envisagés. Dans ce contexte, cette thèse définit deux objectifs: la définition de modèles prédictifs de charge pour la conduite et la conception de modèles d'estimation de charge pour la planification. En ce qui concerne la conduite, nous avons développés deux modèles. Le premier exploite le formalisme mathématique des séries chronologiques ; le second est basé sur un réseau de neurones. Les deux modèles cherchent à prévoir la charge des jours « J+1 » et « J+2 » à partir des informations collectées jusqu'au jour « J ». Le modèle « série chronologique » repose sur les propriétés temporelles des courbes de charge. Ainsi on découpe la courbe de charge en trois parties : la tendance, la périodicité et le résidu. Les premiers deux sont déterministes et indépendamment développés en deux modèles : le modèle de tendance et le modèle de cyclicité. La somme de la prévision de ces deux modèles est la prévision finale. Le résidu quant à lui capture les phénomènes aléatoires que présente la courbe de charge. Le modèle de prédiction ainsi développé s'aide de nombreux outils statistiques (e.g., test de stationnarité, test ANOVA, analyse spectrale, entres autres) pour garantir son bon fonctionnement. Enfin, modèle « série chronologique » prend en compte plusieurs facteurs qui expliquent la variation dans la courbe de consommation tels que la température, les cyclicités, le temps, et le type du jour, etc. En ce qui concerne le modèle à base de réseaux de neurones, nous nous focalisons sur les stratégies de sélection de la structure pour un modèle optimal. Les choix des entrées et du nombre de neurones cachés sont effectués à travers les méthodes dites de «régression orthogonale » et de « leave-one-out-virtuel ». Les résultats montrent que la procédure proposée permet de choisir une structure de réseau de neurones qui garantisse une bonne précision de prédiction. En ce qui concerne la planification, un modèle non paramétrique est proposé et comparé avec le modèle actuel « BAGHEERA » d'EDF. Avec l'ouverture du marché d'électricité, la relation entre les fournisseurs, les clients et les distributeurs devient flexible. Les informations qualitatives d'un client particulier telles que sa puissance souscrite, son code d'activité, ses tarifs etc. sont de moins en moins disponibles. L'évolution du modèle BAGHEERA qui dépend ces informations pour classer les clients dans différentes catégories est devenue indispensable. Le modèle non paramétrique est un modèle individuel centré sur le relevé des compteurs. Trois variables de régression non paramétriques : Nadaraya Watson, Local Linear et Local Linear adapted ont été analysées et comparées. Les scénarios de validation montrent que le modèle non paramétrique est plus précis que le modèle « BAGHEERA ». Ces nouveaux modèles ont été conçus et validés sur de vraies données collectées sur le territoire français. / From 2010, ERDF (French DSO) started the “Linky” project. The project aims at installing 35 millions smart meters in France. These smart meters will collect individual client's consumption data in real time and transfer these data to the data center automatically in a certain frequency. These detailed consumption information provided by the smart metering system enables the designs of more accurate load models. On this purpose, two distinctive objectives are defined in this dissertation: the forecasting load models for the operation need and the estimation load models for the planning need. For the operation need, two models are developed, respectively relying on the “time series” and the “neural network” principals. They are both for the objective of predicting the loads in “D+1” and “D+2” days based on the historical information till “D” day. The “time series” model divides the load curve into three components: the trend, the cyclic, and the residual. The first two parts are deterministic, from which two models named the trend model and the cyclic model are made. The sum of the prevision of these two models is the final prediction result. For a better precision, numerous statistical tools are also integrated such that the stationary test, the smoothed periodogram, the ANOVA test and the gliding window estimation, etc. The time series model can extract information from the influence factors such as the time, the temperature, the periodicities and the day type, etc. Being the most popular non linear model and the universal approximator, the neural network load forecasting model is also studied in this dissertation. We focus on the strategy of the structure selection. The work is in collaboration with Prof. Dreyfus (SIGMA lab), a well known expert in the machine learning field. Input selection and model selection are performed by the “orthogonal forward regression” and the “virtual-leave-one-out” algorithms. Results show that the proposed procedure is efficient and guarantees the chosen model a good accuracy on the load forecasting. For the planning, a nonparametric model is designed and compared with the actual model “BAGHEERA” of the French electricity company EDF. With the opening of the electricity market, the relationship among the regulators, suppliers and clients is changing. The qualitative information about a particular client such as his subscribed power, his activity code and his electricity tariffs becomes less and less available. The evolution from the BAGHEERA model to a data-driven model is unavoidable, since the BAGHEERA model depends on these information to attribute every client in the French territory into a pre-defined category. The proposed nonparametric model is individualized and can deal with both temperature sensitive (possessing an electrical heater) and temperature insensitive clients. Three nonparametric regressors are proposed: the Nadaraya Watson, the local linear, and the local linear adapted. The validation studies show that the nonparametric model has a better estimation precision than the BAGHEERA model. These novel models are designed and validated by the real measurements collected in the French distribution network.

Models and simulators have been widely used in urban contexts for many decades. The drawback of most current models is that they are normally designed for specific objectives, so the elements considered are limited and they do not take into account the potential synergies between related systems. The necessity of a framework to model complex smart city systems with a comprehensive smart city model has been remarked by many authors. Therefore, this PhD thesis presents: i) a general conceptual framework for the modelling of energy related activities in smart cities, based on determining the spheres of influence and intervention areas within the city, and on identifying agents and potential synergies among systems, and ii) the development of a holistic energy model of a smart city for the assessment of different courses of action, given its geo-location, regulatory and technical constraints, and current energy markets. This involves the creation of an optimization model that permits the optimal planning and operation of energy resources within the city. In addition, several analyses were carried out to explore different hypothesis for the smart city energy model, including: a)      an assessment of the importance of including network thermal constraints in the planning and operation of DER systems at a low voltage distribution level, b)      an analysis of aggregator’s market modelling approaches and the impact on prices due to DER aggregation levels, and c)      an analysis of synergies between different systems in a smart city context. Some of the main findings are: It is sensible to not consider network thermal constraints in the planning of DER systems. Results showed that the benefit decrement of considering network constraints was approximatively equivalent to the cost of reinforcing the network when necessary after planning without considering network constraints. The level of aggregation affects the planning and overall benefits of DER systems. Also, price-maker approaches could be more appropriate for the planning and operation of energy resources for medium to large aggregation sizes, but could be unnecessary for small sizes, with low expected impact on the market price. Synergies between different energy systems exist in an interconnected smart city context. Results showed that the overall benefits of a joint management of systems were greater than those of the independently managed systems. Lastly, the smart city energy model was applied to a case study simulating a real smart city implementation, considering five real districts in the southern area of Madrid, Spain. This analysis allowed to assess the potential benefits of the implementation of a real smart city programme, and showed how the proposed smart city energy model could be used for the planning of pilot projects. To the best of our knowledge, such a smart city energy model and modelling framework had not been developed and applied yet, and no economic results in terms of the potential benefits of such a smart city initiative had been previously reported. / <p>QC 20171010</p>

Efficient management of limited water resources in an irrigation reservoir system is necessary to increase crop productivity. To achieve this, a reservoir release policy should be integrated with an optimal crop water allocation. Variations in hydrologic variables such as reservoir inflow, soil moisture, reservoir storage, rainfall and evapotranspiration must be considered in the reservoir operating policy model. Uncertainties due to imprecision, subjectivity, vagueness and lack of adequate data can be handled using the fuzzy set theory. A fuzzy stochastic dynamic programming (FSDP) model with reservoir storage and soil moisture of the crops as fuzzy state variables and inflow as a stochastic variable, is developed to obtain a steady state reservoir operating policy. The model integrates the reservoir operating policy with the crop water allocation decisions by maintaining the storage continuity and the soil moisture balance. The reservoir release decisions are made in the model in 10-day periods and water is allocated to the crops on a daily basis. On comparison with the classical stochastic dynamic programming (SDP) model and a conceptual operation policy model, it is observed that the FSDP model, in general, results in lower release from the reservoir while maintaining lower soil moisture stress. However the steady state reservoir operation policy obtained using the FSDP model may not perform well in a short-term reservoir simulation. A fuzzy state short-term reservoir operation policy model with storage and soil moistures of the crops as fuzzy variables, is developed to obtain a real time release policy using forecasted inflow and forecasted rainfall. The distinguishing features of the model are accounting for (a) spatial variation of soil moisture and rainfall using gridded rainfall forecasts and (b) ponding depth requirement of the Paddy. On comparison with a conceptual operation policy model, the fuzzy state real time operation model is found most suitable for the application of the short term real time operation for irrigation. The real time operation model maintains high storage in the reservoir during most of the 10-day time periods of a year and results in a slightly lower annual releases as compared to the conceptual operation policy model. The effect of inflow forecast uncertainty is examined using different sets of forecasted inflows, and it is observed that the system performance is quite sensitive to inflow forecast uncertainties. The use of the satellite based gridded soil moisture in the real time operation model shows consideration of realistic situations. Further, three performance measures, viz., fuzzy reliability, fuzzy resiliency and fuzzy vulnerability are developed to evaluate the performance of the irrigation reservoir system under a specified operating policy. A fuzzy set with an appropriate membership function is defined to describe the working and failed states to account for the system being in partly working and partly failed state. The degree of failure of the irrigation reservoir system is defined based on the evapotranspiration deficit in a period. Inclusion of fuzziness in the performance measures enables realistic representation of uncertainties in the state of the system. A case study of Bhadra reservoir system in Karnataka, India is chosen for demonstrating the model applications.

建構優質的電子化政府，首要條件是提供民眾整合型一站式服務的入口網站，達成一站式服務，除共用基礎設施服務外，應具備介接整合各機關資訊與線上服務的能力。過往政府推動電子化政府，雖然已獲得相當成就，但各機關資訊系統整合程度不足，多數服務尚無法提供跨機關主動服務，本研究是藉由文獻的蒐集與研究，並以創新的思維，提出政府主動服務的經營模式，並採個案研究方法，驗證所提出之模式的可行性，因此，在研究方法上，將著重現實現象的整理與未來環境、機制的設計，即採行從當前環境到期望環境藍圖(From AS-IS to TO-BE)策略分析的研究模式。 本研究結果發現：1.各國政府以實現「全政府」作為主要策略，顯示推動資訊系統整合仍為主要工作。2.藉由Web2.0 使用戶間強大連結力，企業可以快速產生新的商業模式，提升企業網站的知名度與使用度。3.本研究提出改變電子化政府服務型態的三要素，資訊代理人、以家戶為主體、為顧客提供Web2.0平台。4. 本研究將網站的服務分為九種型態，提供網站服務轉型參考。5.本研究提出以CRM建構前瞻性主動服務的理念，整合內部資訊與顧客喜好，主動提供顧客服務。 政府Myegov入口網與民眾e管家，本研究提出主動服務的改造建議有：1.Myegov入口網應提供元件共享、資訊透過RSS互通。2.提供跨機關主題式服務、提供民眾可自行訂製、設計自己符合自己需要的e政府管家。3.以「家」為主體的理念，引導政府透過政府共享式服務平台，主動整合後台資訊系統。4.運用資料倉儲系統，建立市民關係管理共享式資料庫，提供民眾前瞻性主動服務。 / In order to construct a high-quality e-government, an integrated one-stop-shop service portal must be provided to the general public. In addition to sharing the services of IT infrastructure, to achieve the integrated one-stop service is also necessary to provide interfaces for all departments so that their information and services can be integrated. In the past, the government has reached many achievements in transforming many of the governmental related processes electronically. However, the integrated information system between different levels and/or ministries is not enough to provide the general public certain interdepartmental services actively. It is the hope of this research to discover an efficient strategy to push for better proactive e-government service model and to provide such strategy to the government for further reference. According to this research is hoping to use innovative thinking to come up the proactive e-government service model based on the collection and research of various documents and literature. This study will also use related e-government research cases for feasibility study on the model proposed. As such, this research stressed on current condition and future context, the design of mechanism, employing in “As-Is to To-Be” strategic analysis research model. This result of study is found: 1.The governments of various countries promote the e-government's main strategy as the future service was still realizing on the information-intensive society of “the whole government”. It shows that various countries are pushing the work on information system integration. 2. Web2.0 is not only a new information technology, but the mechanism that enterprises are used for interacting with end user and offer the space to user by sharing information at the same time. According to leverage the linkage through powerful strength on Web2.0 service use, enterprises can produce the new business model timely and improving enterprise website’s popularity and utilized degree. 3. To create the innovative e-government service, this research proposed three key elements to change government's service type and performance measurement, including “the information agent”, “relies mainly on family one”, “offers Web2.0 service platform to customer”. 4. This research based on web information providing method and service attribute to defined the nine kinds of service model to transforming the portal service as proactive service type. 5. The research proposed and constructed the “customer first” proactive service theory and integrated the customer relevant information and favor internally to providing many of services proactively. Moreover, based on Myegov portal and e-housekeeper, the research proposed active service transformation items are: 1. Myegov portal should provide Portlet-based shared components, RSS subscribing and exchanging service. 2. Offering the topic type’s Blog service by cross departments; offering general public to define, design their own custom e-housekeeper services through e-government. 3. According to “the family” theme’s concept, government should provide the integrated back-end IT system through the shared service platform actively. 4. Use the data warehouse system to gather the department and people relevant information, and build up the shared database on customer relationship management system to provide the “customer first” proactive service.

Optimal operation of reservoir systems is necessary for better utilizing the limited water resources and to justify the high capital investments associated with reservoir projects. However, finding optimal policies for real-life problems of reservoir systems operation (RSO) is a challenging task as the available analytical methods can not handle the arbitrary functions of the problem and almost all methods employed are numerical or iterative type that are computer dependent. Since the computer resources in terms of memory and CPU time are limited, a limit exists for the size of the problem, in terms of arithmetic and memory involved, that can be handled. This limit is approached quickly as the dimension and the nonlinearity of the problem increases. In encountering the complex aspects of the problem all the traditionally employed methods have their own drawbacks. Linear programming (LP), though very efficient in dealing with linear functions, can not handle nonlinear functions which is the case mostly in real-life problems. Attempting to approximate nonlinear functions to linear ones results in the problem size growing enormously. Dynamic programming (DP), though suitable for most of the RSO problems, requires exponentially increasing computer resources as the dimension of the problem increases and at present many high dimensional real-life problems can not be solved using DP. Nonlinear programming (NLP) methods are not known to be efficient in RSO problems due to slow rate of convergence and inability to handle stochastic problems. Simulation methods can, practically, explore only a small portion of the search region. Many simplifications in formulations and adoption of approximate methods in literature still fall short in addressing the most critical aspects, namely multidimensionality, stochasticity, and additional complexity in conjunctive operation, of the problem. As the problem complexity increases and the possibility of arriving at the solution recedes, a near optimal solution with the best use of computational resources can be very valuable. In this context, genetic algorithms (GA) can be a promising technique which is believed to have an advantage in terms of efficient use of computer resources. GA is a random search method which find, in general, near optimal solutions using evolutionary mechanism of natural selection and natural genetics. When a pool of feasible solutions, represented in a coded form, are given fitness according to a objective function and explored by genetic operators for obtaining new pools of solutions, then the ensuing trajectories of solutions come closer and closer to the optimal solution which has the greatest fitness associated with it. GA can be applied to arbitrary functions and is not excessively sensitive to the dimension of the problem. Though in general GA finds only the near optimal solutions trapping in local optima is not a serious problem due to global look and random search. Since GA is not fully explored for RSO problems two such problems are selected here to study the usefulness and efficiency of GA in obtaining near optimal solutions. One problem is conjunctive operation of a system consisting of a surface reservoir and an aquifer, taken from the literature for which deterministic and stochastic models are solved. Another problem is real-time operation of a multipurpose reservoir, operated for irrigation (primary purpose) and hydropower production, which is in the form of a case study. The conjunctive operation problem consists of determining optimal policy for a combined system of a surface reservoir and an aquifer. The surface reservoir releases water to an exclusive area for irrigation and to a recharge facility from which it reaches the aquifer in the following period. Another exclusive area is irrigated by water pumped from the aquifer. The objective is to maximize the total benefit from the two irrigated areas. The inflow to the surface reservoir is treated as constant in deterministic model and taken at 6 different classes in stochastic model. The hydrological interactions between aquifer and reservoir are described using a lumped parameter model in which the average aquifer water table is arrived at based on the quantity of water in the aquifer, and local drawdown in pumping well is neglected. In order to evaluate the GA solution both deterministic and stochastic models are solved using DP and stochastic DP (SDP) techniques respectively. In the deterministic model, steady state (SS) cyclic (repetitive) solution is identified in DP as well as in GA. It is shown that the benefit from GA solution converges to as near as 95% of the benefit from exact DP solution at a highly discounted CPU time. In the stochastic model, the steady state solution obtained with SDP consists of converged first stage decisions, which took a 8-stage horizon, for any combination of components of the system state. The GA solution is obtained after simplifying the model to reduce the number of decision variables. Unlike SDP policy which gives decisions considering the state of the system in terms of storages, at reservoir, aquifer, and recharge facility, and previous inflow at the beginning of that period, GA gives decisions for each period of the horizon considering only the past inflow state of the period. In arriving at these decisions the effect of neglected state information is approximately reflected in the decisions by the process of refinement of the decisions, to conform to feasibility of storages in reservoir and aquifer, carried out in a simplified simulation process. Moreover, the validity of the solution is confirmed by simulating the operation with all possible inflow sequences for which the 8-stages benefit converged up to 90 % of the optimum. However, since 8 stages are required for convergence to SS, a 16-stage process is required for GA method in which the first 8 stages policy is valid. Results show that GA convergence to the optimum is satisfactory, justifying the approximations, with significant savings in CPU time. For real-time operation of a multipurpose reservoir, a rule curve (RC) based monthly operation is formulated and applied on a real-life problem involving releases for irrigation as well as power production. The RC operation is based on the target storages that have to be maintained, at each season of the year, in the reservoir during normal hydrological conditions. Exceptions to target storages are allowed when the demands have to be met or for conserving water during the periods of high inflows. The reservoir in the case study supplies water to irrigation fields through two canals where a set of turbines each at the canal heads generate hydropower. A third set of turbines operate on the river bed with the water let out downstream from the dam. The problem consists of determining the the RC target storages that facilitate maximum power production while meeting the irrigation demands up to a given reliability level. The RC target storages are considered at three different levels, corresponding to dry, normal, and wet conditions, according to the system state in terms of actual (beginning of period) storage of the reservoir. That is, if the actual beginning storage of the reservoir is less than some coefficient, dry-coe, times the normal target storage the target for the end of the period storage is taken at the dry storage target (of the three sets of storages). Similarly the wet level is taken for the end of the period target if the actual beginning storage is greater than some coefficient, wet-coe, times the normal storage. For other conditions the target is the normal storage level. The dry-coe and wet-coe parameters are obtained by trial and error analysis working on a small sequence of inflows. The three sets of targets are obtained from optimization over a 1000 year generated inflow sequence. With deterministic DP solutions, for small sequences of inflows, the optimization capability of GA-RC approach, in terms of objective function convergence, and generalization or robustness capability of GA-RC approach, for which the GA-RC benefit is obtained by simulating the reservoir operation using the previously obtained GA-RC solution, are evaluated. In both the cases GA-RC approach proves to be promising. Finally a 15 year real-time simulation of the reservoir is carried out using historical inflows and demands and the comparison with the historical operation shows significant improvement in benefit, i.e. power produced, without compromising irrigation demands throughout the simulation period.