Percorrer por autor "Correia, Regina"
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- Electricity Consumption Forecast in an Industry Facility to Support Production Planning Update in Short TimePublication . Ramos, Daniel; Faria, Pedro; Vale, Zita; Correia, ReginaThe global environmental concerns raise the need to decrease energy, namely electricity consumption. Energy consumption can be reduced by improving energy efficiency and by improving the optimization of energy management in each context. These opportunities are very relevant in buildings and industry facilities. In order to improve the optimized energy management, adequate forecasting tools are needed regarding the load consumption patterns in each building. In the present paper, two forecasting technics, namely neural networks, and support vector machine, are used to predict the consumption of an industry facility for each 5 minutes. The proposed model finds the best method in order to be used in a later stage regarding the updated of production planning. The size of historic data is also discussed. The case study includes one-week test data and more than one-year train data
- Industrial Facility Electricity Consumption Forecast Using Artificial Neural Networks and Incremental LearningPublication . Ramos, Daniel; Faria, Pedro; Vale, Zita; Mourinho, João; Correia, ReginaSociety’s concerns with electricity consumption have motivated researchers to improve on the way that energy consumption management is done. The reduction of energy consumption and the optimization of energy management are, therefore, two major aspects to be considered. Additionally, load forecast provides relevant information with the support of historical data allowing an enhanced energy management, allowing energy costs reduction. In this paper, the proposed consumption forecast methodology uses an Artificial Neural Network (ANN) and incremental learning to increase the forecast accuracy. The ANN is retrained daily, providing an updated forecasting model. The case study uses 16 months of data, split in 5-min periods, from a real industrial facility. The advantages of using the proposed method are illustrated with the numerical results
- Production Line Optimization to Minimize Energy Cost and Participate in Demand Response EventsPublication . Mota, Bruno; Gomes, Luis; Faria, Pedro; Ramos, Carlos; Vale, Zita; Correia, ReginaThe scheduling of tasks in a production line is a complex problem that needs to take into account several constraints, such as product deadlines and machine limitations. With innovative focus, the main constraint that will be addressed in this paper, and that usually is not considered, is the energy consumption cost in the production line. For that, an approach based on genetic algorithms is proposed and implemented. The use of local energy generation, especially from renewable sources, and the possibility of having multiple energy providers allow the user to manage its consumption according to energy prices and energy availability. The proposed solution takes into account the energy availability of renewable sources and energy prices to optimize the scheduling of a production line using a genetic algorithm with multiple constraints. The proposed algorithm also enables a production line to participate in demand response events by shifting its production, by using the flexibility of production lines. A case study using real production data that represents a textile industry is presented, where the tasks for six days are scheduled. During the week, a demand response event is launched, and the proposed algorithm shifts the consumption by changing task orders and machine usage.
- Short Time Electricity Consumption Forecast in an Industry FacilityPublication . Ramos, Daniel; Faria, Pedro; Vale, Zita; Correia, ReginaThe work in this article uses artificial neural networks and support vector machine to forecast electricity consumption in an industrial facility. The main objective is to show that such a problem should be treated with a contextual approach that identifies the most adequate technic in each moment for a single building, contrary to the previous works in the literature that compare the accuracy of each method for the complete data set representing aggregated loads. 72 different algorithms have been implemented and tested. After that, the three most suitable ones are selected in order to support the automated decisions of the best algorithm according to the context. In this way, the implemented methodology finds the best method for the prediction of each 5 min. It can be later used to update the production planning in the industrial facility. It also discussed the size of historical data and the most suitable learning parameters for each method. The case study includes test data for one week and more than one year of training data.