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handle: 10017/53172
En el presente TFG se comparan diferentes soluciones para la predicción del índice de calidad del aire a partir de un modelo paramétrico no lineal identificado con datos reales registrados cada minuto y procesados mediante la técnica SSA (Singular Spectral Analysis). Se analizan pros y contras de la adaptación del modelo, manteniendo la estructura, con diferentes horizontes de predicción, desde una hora (H=60 muestras) hasta seis horas (H=360 muestras), mostrando resultados gráficos y tabulados. Se concluye, como mejor opción, la predicción de IAQ mediante un modelo adaptativo con horizonte de predicción dinámico.
In the present TFG it is compared different solutions for predicting air quality (IAQ) based on non linear parametric model identified with real data registered every minute and processed by SSA (Singular Spectral Analysis). It is analized pros and cons of the adjustment of the model, keeping the same structure, with different prediction horizon, for 1 hour (60 samples) till 6 hours (360 samples), showing graphics and tabular results. It is concluded as the beast option, the prediction of IAQ by an adaptive model with dynamic prediction horizon.
Grado en Ingeniería Electrónica de Comunicaciones
Modelo paramétrico no lineal estático, Identificación recursiva, Time series, Static nonlinear parametric model, Dynamic prediction horizon, Serie temporal, Electrónica, Electronics, SSA, Recursive identification, Horizonte de predicción dinámico
Modelo paramétrico no lineal estático, Identificación recursiva, Time series, Static nonlinear parametric model, Dynamic prediction horizon, Serie temporal, Electrónica, Electronics, SSA, Recursive identification, Horizonte de predicción dinámico
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