Persona: Duro Carralero, Natividad
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Duro Carralero
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Natividad
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Publicación Advanced Control by Reinforcement Learning for Wastewater Treatment Plants: A Comparison with Traditional Approaches(MDPI, 2023) Gorrotxategi Zipitria, Mikel; Hernández del Olmo, Félix; Gaudioso Vázquez, Elena; Duro Carralero, Natividad; Dormido Canto, RaquelControl mechanisms for biological treatment of wastewater treatment plants are mostly based on PIDS. However, their performance is far from optimal due to the high non-linearity of the biological and changing processes involved. Therefore, more advanced control techniques are proposed in the literature (e.g., using artificial intelligence techniques). However, these new control techniques have not been compared to the traditional approaches that are actually being used in real plants. To this end, in this paper, we present a comparison of the PID control configurations currently applied to control the dissolved oxygen concentration (in the active sludge process) against a reinforcement learning agent. Our results show that it is possible to have a very competitive operating cost budget when these innovative techniques are applied.Publicación Tackling the start-up of a reinforcement learning agent for the control of wastewater treatment plants(Elsevier, 2018) Hernández del Olmo, Félix; Gaudioso Vázquez, Elena; Dormido Canto, Raquel; Duro Carralero, NatividadReinforcement learning problems involve learning by doing. Therefore, a reinforcement learning agent will have to fail sometimes (while doing) in order to learn. Nevertheless, even with this starting error, introduced at least during the non-optimal learning stage, reinforcement learning can be affordable in some domains like the control of a wastewater treatment plant. However, in wastewater treatment plants, trying to solve the day-to-day problems, plant operators will usually not risk to leave their plant in the hands of an inexperienced and untrained reinforcement learning agent. In fact, it is somewhat obvious that plant operators will require firstly to check that the agent has been trained and that it works as it should at their particular plant. In this paper, we present a solution to this problem by giving a previous instruction to the reinforcement learning agent before we let it act on the plant. In fact, this previous instruction is the key point of the paper. In addition, this instruction is given effortlessly by the plant operator. As we will see, this solution does not just solve the starting up problem of leaving the plant in the hands of an untrained agent, but it also improves the future performance of the agent.Publicación Machine Learning Weather Soft-Sensor for Advanced Control ofWastewater Treatment Plants(MDPI, 2019) Hernández del Olmo, Félix; Gaudioso Vázquez, Elena; Duro Carralero, Natividad; Dormido Canto, RaquelControl of wastewater treatment plants (WWTPs) is challenging not only because of their high nonlinearity but also because of important external perturbations. One the most relevant of these perturbations is weather. In fact, different weather conditions imply different inflow rates and substance (e.g., N-ammonia, which is among the most important) concentrations. Therefore, weather has traditionally been an important signal that operators take into account to tune WWTP control systems. This signal cannot be directly measured with traditional physical sensors. Nevertheless, machine learning-based soft-sensors can be used to predict non-observable measures by means of available data. In this paper, we present novel research about a new soft-sensor that predicts the current weather signal. This weather prediction differs from traditional weather forecasting since this soft-sensor predicts the weather conditions as an operator does when controling the WWTP. This prediction uses a model based on past WWTP influent states measured by only a few physical and widely applied sensors. The results are encouraging, as we obtained a good accuracy level for a relevant and very useful signal when applied to advanced WWTP control systems.Publicación Multi-Step Clustering of Smart Meters Time Series: Application to Demand Flexibility Characterization of SME Customers(Tech Science Press, 2024-12-17) Bañales López, Santiago; Dormido Canto, Raquel; Duro Carralero, NatividadCustomer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’ participation in the energy transition. This study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons. Smart meter data is split between daily and hourly normalized time series to assess monthly, weekly, daily, and hourly seasonality patterns separately. The dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series data. The intraday clustering procedure sequentially identifies representative hourly day-unit profiles for each customer and the entire population. For the first time, a step function approach is applied to reduce time series dimensionality. Customer attributes embedded in surveys are employed to build external clustering validation metrics using Cramer’s V correlation factors and to identify statistically significant determinants of load-shape in energy usage. In addition, a time series features engineering approach is used to extract 16 relevant demand flexibility indicators that characterize customers and corresponding clusters along four different axes: available Energy (E), Temporal patterns (T), Consistency (C), and Variability (V). The methodology is implemented on a real-world electricity consumption dataset of 325 Small and Medium-sized Enterprise (SME) customers, identifying 4 daily and 6 hourly easy-to-interpret, well-defined clusters. The application of the methodology includes selecting key parameters via grid search and a thorough comparison of clustering distances and methods to ensure the robustness of the results. Further research can test the scalability of the methodology to larger datasets from various customer segments (households and large commercial) and locations with different weather and socioeconomic conditions.Publicación Smart meters time series clustering for demand response applications in the context of high penetration of renewable energy resources(MDPI, 2021-06-11) Bañales López, Santiago; Dormido Canto, Raquel; Duro Carralero, NatividadThe variability in generation introduced in the electrical system by an increasing share of renewable technologies must be addressed by balancing mechanisms, demand response being a prominent one. In parallel, the massive introduction of smart meters allows for the use of high frequency energy use time series data to segment electricity customers according to their demand response potential. This paper proposes a smart meter time series clustering methodology based on a two-stage k-medoids clustering of normalized load-shape time series organized around the day divided into 48 time points. Time complexity is drastically reduced by first applying the k-medoids on each customer separately, and second on the total set of customer representatives. Further time complexity reduction is achieved using time series representation with low computational needs. Customer segmentation is undertaken with only four easy-to-interpret features: average energy use, energy–temperature correlation, entropy of the load-shape representative vector, and distance to wind generation patterns. This last feature is computed using the dynamic time warping distance between load and expected wind generation shape representative medoids. The two-stage clustering proves to be computationally effective, scalable and performant according to both internal validity metrics, based on average silhouette, and external validation, based on the ground truth embedded in customer surveys.