Multi-criteria methodology based on data science for the selection of the optimal forecast model for residential electricity consumption

There is a wide variety of techniques and models for forecasting electrical energy consumption, depending on both the type of user, the forecast horizon, and the resolution of the available data. Likewise, there are different metrics to evaluate the performance of these models. So, in this research an integrated multi-criteria methodology is proposed to select the best forecast model for residential electricity consumption, using the Analytical Hierarchical Process (AHP) to establish the weights of relative importance of the decision criteria, and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to make the selection of the optimal model. The methodology is in turn framed within a data science process, through which the data is extracted, processed, and analyzed, prior to the application of the machine learning algorithms to obtain the forecast models, which will correspond to decision alternatives. The performance metrics in the evaluation phase of the models, and the performance metrics obtained from the forecast phase, are considered as the decision criteria. From the pairwise comparisons technique, it was obtained that the mean absolute percentage error (MAPE) of the prognosis phase was the criterion with the greatest weight of importance, followed by the coefficient of determination R2 and the MAPE of the evaluation phase. From the TOPSIS method, the Multiple Linear Regression model was selected as the optimal forecast model.  Existe una gran variedad de técnicas y modelos para el pronóstico del consumo de energía eléctrica, dependiendo tanto del tipo de usuario, como del horizonte de pronóstico y de la resolución de los datos disponibles. Asimismo, existen distintas métricas para evaluar el desempeño de estos modelos. Entonces, en esta investigación se propone una metodología integrada multicriterio para seleccionar el mejor modelo de pronóstico del consumo de energía eléctrica residencial, utilizando el proceso jerárquico analítico (AHP) para establecer los pesos de importancia relativa de los criterios de decisión, y la técnica para el orden de preferencia por similitud con la solución ideal  (TOPSIS) para hacer la selección del modelo óptimo. La metodología se enmarca a su vez dentro de un proceso de ciencia de datos, a través del cual se extraen, procesan y analizan los datos, previo a la aplicación de los algoritmos de aprendizaje automático para obtener los modelos de pronósticos, que se corresponderán con las alternativas de decisión. Las métricas de desempeño en la fase de evaluación de los modelos, y las métricas de desempeño obtenidas de la fase de pronóstico, son consideradas como los criterios de decisión. De la técnica de comparaciones pareadas se obtuvo que el error porcentual absoluto medio (MAPE) de la fase de pronóstico fue el criterio con mayor peso de importancia, seguido del coeficiente de determinación R2 y del MAPE de la fase de evaluación. A partir del método TOPSIS, se seleccionó el modelo de Regresión Lineal Múltiple como el modelo óptimo de pronóstico

Multi-criteria methodology based on data science for the selection of the optimal forecast model for residential electricity consumption

There is a wide variety of techniques and models for forecasting electrical energy consumption, depending on both the type of user, the forecast horizon, and the resolution of the available data. Likewise, there are different metrics to evaluate the performance of these models. So, in this research an integrated multi-criteria methodology is proposed to select the best forecast model for residential electricity consumption, using the Analytical Hierarchical Process (AHP) to establish the weights of relative importance of the decision criteria, and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to make the selection of the optimal model. The methodology is in turn framed within a data science process, through which the data is extracted, processed, and analyzed, prior to the application of the machine learning algorithms to obtain the forecast models, which will correspond to decision alternatives. The performance metrics in the evaluation phase of the models, and the performance metrics obtained from the forecast phase, are considered as the decision criteria. From the pairwise comparisons technique, it was obtained that the mean absolute percentage error (MAPE) of the prognosis phase was the criterion with the greatest weight of importance, followed by the coefficient of determination R2 and the MAPE of the evaluation phase. From the TOPSIS method, the Multiple Linear Regression model was selected as the optimal forecast model.  Existe una gran variedad de técnicas y modelos para el pronóstico del consumo de energía eléctrica, dependiendo tanto del tipo de usuario, como del horizonte de pronóstico y de la resolución de los datos disponibles. Asimismo, existen distintas métricas para evaluar el desempeño de estos modelos. Entonces, en esta investigación se propone una metodología integrada multicriterio para seleccionar el mejor modelo de pronóstico del consumo de energía eléctrica residencial, utilizando el proceso jerárquico analítico (AHP) para establecer los pesos de importancia relativa de los criterios de decisión, y la técnica para el orden de preferencia por similitud con la solución ideal  (TOPSIS) para hacer la selección del modelo óptimo. La metodología se enmarca a su vez dentro de un proceso de ciencia de datos, a través del cual se extraen, procesan y analizan los datos, previo a la aplicación de los algoritmos de aprendizaje automático para obtener los modelos de pronósticos, que se corresponderán con las alternativas de decisión. Las métricas de desempeño en la fase de evaluación de los modelos, y las métricas de desempeño obtenidas de la fase de pronóstico, son consideradas como los criterios de decisión. De la técnica de comparaciones pareadas se obtuvo que el error porcentual absoluto medio (MAPE) de la fase de pronóstico fue el criterio con mayor peso de importancia, seguido del coeficiente de determinación R2 y del MAPE de la fase de evaluación. A partir del método TOPSIS, se seleccionó el modelo de Regresión Lineal Múltiple como el modelo óptimo de pronóstico

Funding sources : problem or solution?

Aligned with the goals of the Mexican National Development Plan, the state of Jalisco, one of the 32 Federal Entities of Mexico, is rapidly developing. In order to maintain a proper management of public debt, the government of Jalisco has sought a viable instrument to promote economic growth by generating infrastructure that have a direct impact and that benefit the population. In this paper, Mr Jorge Aristóteles Sandoval Díaz, Governor of the State of Jalisco, describes the significant adjustments that his government implemented aiming at restructuring state finances and strengthening austerity measures in public spending in order to allocate the resources to prioritizing actions for the benefit of the citizens. In particular, he focuses on the local government's commitment to give new impetus to investment in infrastructural projects thanks to the sectoral Program called "Movilidad Sustentable" (Sustainable Mobility). This Program looks at the interaction of the public transport with the other actors of mobility (pedestrians, cyclists and car drivers) and sets a model for reorganization and improvement. More concretely, the plan for the "Extension and Modernization of Light Rail Line 1 of Guadalajara" is part of this Program and serves as the case study that Mr Sandoval Díaz uses to explain the idea that public funding can be a trigger for economic development, as long as it is responsibly administered

Aristoteles stagiritae philosophorum principis De coelo libri IIII : ex graeco iuxta traslationem veterem accuratissime recogniti : a D. Thomae Aquinate praeclarissimis commentarijs illustrati : iis vero quae à D. Thoma interpretari non potuerunt, adiecta est Petri Aluerniatis non obscura expositio : cum indice duplici...

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Política / Aristóteles

Divulgação dos SUMÁRIOS das obras recentemente incorporadas ao acervo da Biblioteca Ministro Oscar Saraiva do STJ. Em respeito à Lei de Direitos Autorais, não disponibilizamos a obra na íntegra.Localização na estante: 321.01 A717p

Metafisica

Contiene: I. Libros I al X -- II. Ética eudémica ; De las virtudes y vicio

SUR1 Receptor Interaction with Hesperidin and Linarin Predicts Possible Mechanisms of Action of Valeriana officinalis in Parkinson.

Parkinson's disease (PD) is one of the most common neurodegenerative disorders. A theoretical approach of our previous experiments reporting the cytoprotective effects of the Valeriana officinalis compounds extract for PD is suggested. In addiction to considering the PD as a result of mitochondrial metabolic imbalance and oxidative stress, such as in our previous in vitro model of rotenone, in the present manuscript we added a genomic approach to evaluate the possible underlying mechanisms of the effect of the plant extract. Microarray of substantia nigra (SN) genome obtained from Allen Brain Institute was analyzed using gene set enrichment analysis to build a network of hub genes implicated in PD. Proteins transcribed from hub genes and their ligands selected by search ensemble approach algorithm were subjected to molecular docking studies, as well as 20 ns Molecular Dynamics (MD) using a Molecular Mechanic Poison/Boltzman Surface Area (MMPBSA) protocol. Our results bring a new approach to Valeriana officinalis extract, and suggest that hesperidin, and probably linarin are able to relieve effects of oxidative stress during ATP depletion due to its ability to binding SUR1. In addition, the key role of valerenic acid and apigenin is possibly related to prevent cortical hyperexcitation by inducing neuronal cells from SN to release GABA on brain stem. Thus, under hyperexcitability, oxidative stress, asphyxia and/or ATP depletion, Valeriana officinalis may trigger different mechanisms to provide neuronal cell protection.Fil: Santos, Gesivaldo. Universidade Estadual do Sudoeste da Bahia; BrasilFil: Giraldez Alvarez, Lisandro Diego. Universidade Estadual do Sudoeste da Bahia; BrasilFil: Ávila Rodriguez, Marco. Pontificia Universidad Javeriana; ColombiaFil: Capani, Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Cardiológicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Cardiológicas; ArgentinaFil: Galembeck, Eduardo. Universidade Estadual de Campinas; BrasilFil: Gôes Neto, Aristóteles. Universidade Estadual de Feira de Santana; BrasilFil: Barreto, George E.. Pontificia Universidad Javeriana; ColombiaFil: Andrade, Bruno. Universidade Estadual do Sudoeste da Bahia; Brasi

Título: Ethica ad Nicomachum

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