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As a consequence of the projected world population growth, world meat consumption is expected to grow. Therefore, meat production needs to be improved, although it cannot be done at any cost. Maintaining the health and welfare status of animals at optimal levels has traditionally been a main concern of farmers, and more recently, consumers. In this article the Poultry Chain Management (PCM) platform is presented. It aims at collecting data across the different phases of the poultry production chain. The collection of this data not only contributes to determine the quality of each phase and the poultry production chain as a whole, but more importantly, to identify critical issues causing process inefficiencies and to support decision-making towards the holistic improvement of the production chain. Results showed that the information gathered can be exploited to make different suggestions to guarantee poultry welfare, and ultimately, improve the quality of the meat.

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RESPOND proposes an Artificial Intelligent (AI) system to assist residential consumers that would like to make use of Demand Response (DR) and incorporate it into their energy management systems. The proposed system considers the forecast energy consumption based on the data acquired. This work compares the results obtained by different forecasting methods using the Root Mean Square Error (RMSE) as a measure of the forecast performance. The ARIMA, Linear Regression (LR), Support Vector Regression (SVR) and k-Nearest Neighbors (KNN) models are tested, and it is concluded that the results achieved with the KNN obtain a better fit. In addition to obtaining the lowest RMSE, KNN is the algorithm that spends less time in obtaining the forecasts.

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