Intelligent energy storage management trade-off system applied to Deep Learning predictions

The control of the electrical power supply is one of the key bases to reach the sustainable development goals set by United Nations. The achievement of these objectives encourages a dual strategy of creation and diffusion of renewable energies and other technologies of zero emission. Thus, meet the emerging necessities require, inevitably, a significant transformation of the building sector to improve the design of the electrical infrastructure. This improvement should be linked to advanced techniques that allows the identification of complex patterns in large amount of data, such as Deep Learning ones, in order to mitigate potential uncertainties. Accurate electricity and energy supply prediction models, in combination with storage systems will be reflected directly in efficiency improvements in buildings. In this paper, a branch of Deep Learning models, known as Standard Neural Networks, are used to predict electricity consumption and photovoltaic generation with the purpose of reduce the energy wasted, by managing the storage system using Reinforcement Learning technique. Specifically, Deep Reinforcement Learning is applied using the Deep Q-Learning agent. Furthermore, the accuracy of the predicted variables is measured by means of normalized Mean Bias Error (nMBE), and normalized Root Mean Squared Error (nRMSE). The methodologies developed are validated in an existing building, the School of Mining and Energy Engineering located on the Campus of the University of Vigo.Agencia Estatal de Investigación | Ref. TED2021-130677B-I00Financiado para publicación en acceso aberto: Universidade de Vigo/CISU

Feasibility of different weather data sources applied to building indoor temperature estimation using LSTM neural networks

The use of Machine Learning models is becoming increasingly widespread to assess energy performance of a building. In these models, the accuracy of the results depends largely on outdoor conditions. However, getting these data on-site is not always feasible. This article compares the temperature results obtained for an LSTM neural network model, using four types of meteorological data sources. The first is the monitoring carried out in the building; the second is a meteorological station near the site of the building; the third is a table of meteorological data obtained through a kriging process and the fourth is a dataset obtained using GFS. The results are analyzed using the CV(RSME) and NMBE indices. Based on these indices, in the four series, a CV(RSME) slightly higher than 3% is obtained, while the NMBE is below 1%, so it can be deduced that the sources used are interchangeable.Ministerio de Ciencia, Innovación y Universidades | Ref. RTI2018-096296-B-C

Use of Optimised LSTM Neural Networks Pre-Trained With Synthetic Data to Estimate PV Generation

Optimising the use of the photovoltaic (PV) energy is essential to reduce fossil fuel emissions by increasing the use of solar power generation. In recent years, research has focused on physical simulations or artifical intelligence models attempting to increase the accuracy of PV generation predictions. The use of simulated data as pre-training for deep learning models has increased in different fields. The reasons are the higher efficiency in the subsequent training with real data and the possibility of not having real data available. This work presents a methodology, based on an deep learning model optimised with specific techniques and pre-trained with synthetic data, to estimate the generation of a PV system. A case study of a photovoltaic installation with 296 PV panels located in northwest Spain is presented. The results show that the model with proper pre-training trains six to seven times faster than a model without pre-training and three to four times faster than a model pre-trained with non-accurate simulated data. In terms of accuracy and considering a homogeneous training process, all models obtained average relative errors around 12%, except the model with incorrect pre-training which performs worse

Machine learning and deep learning models applied to photovoltaic production forecasting

The increasing trend in energy demand is higher than the one from renewable generation, in the coming years. One of the greatest sources of consumption are buildings. The energy management of a building by means of the production of photovoltaic energy in situ is a common alternative to improve sustainability in this sector. An efficient trade-off of the photovoltaic source in the fields of Zero Energy Buildings (ZEB), nearly Zero Energy Buildings (nZEB) or MicroGrids (MG) requires an accurate forecast of photovoltaic production. These systems constantly generate data that are not used. Artificial Intelligence methods can take advantage of this missing information and provide accurate forecasts in real time. Thus, in this manuscript a comparative analysis is carried out to determine the most appropriate Artificial Intelligence methods to forecast photovoltaic production in buildings. On the one hand, the Machine Learning methods considered are Random Forest (RF), Extreme Gradient Boost (XGBoost), and Support Vector Regressor (SVR). On the other hand, Deep Learning techniques used are Standard Neural Network (SNN), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN). The models are checked with data from a real building. The models are validated using normalized Mean Bias Error (nMBE), normalized Root Mean Squared Error (nRMSE), and the coefficient of variation (R2). Standard deviation is also used in conjunction with these metrics. The results show that the models forecast the test set with errors of less than 2.00% (nMBE) and 7.50% (nRMSE) in the case of considering nights, and 4.00% (nMBE) and 11.50% (nRMSE) if nights are not considered. In both situations, the R2 is greater than 0.85 in all models.Universidade de Vigo | Ref. 00VI 131H 641021

Load forecasting with machine learning and deep learning methods

Characterizing the electric energy curve can improve the energy efficiency of existing buildings without any structural change and is the basis for controlling and optimizing building performance. Artificial Intelligence (AI) techniques show much potential due to their accuracy and malleability in the field of pattern recognition, and using these models it is possible to adjust the building services in real time. Thus, the objective of this paper is to determine the AI technique that best forecasts electrical loads. The suggested techniques are random forest (RF), support vector regression (SVR), extreme gradient boosting (XGBoost), multilayer perceptron (MLP), long short-term memory (LSTM), and temporal convolutional network (Conv-1D). The conducted research applies a methodology that considers the bias and variance of the models, enhancing the robustness of the most suitable AI techniques for modeling and forecasting the electricity consumption in buildings. These techniques are evaluated in a single-family dwelling located in the United States. The performance comparison is obtained by analyzing their bias and variance by using a 10-fold cross-validation technique. By means of the evaluation of the models in different sets, i.e., validation and test sets, their capacity to reproduce the results and the ability to properly forecast on future occasions is also evaluated. The results show that the model with less dispersion, both in the validation set and test set, is LSTM. It presents errors of −0.02% of nMBE and 2.76% of nRMSE in the validation set and −0.54% of nMBE and 4.74% of nRMSE in the test set.Universidade de Vigo | Ref. 00VI 131H 6410211European Group for territorial cooperation Galicia-North of Portugal (GNP, AECT) through the IACOBUS program of research staysMinisterio de Ciencia, Innovación y Universidades | Ref. FPU19/01187Ministerio de Ciencia, Innovación y Universidades | Ref. TED2021-130677B-I0

A functional data analysis for assessing the impact of a retrofitting in the energy performance of a building

There is an increasing interest in reducing the energy consumption in buildings and in improving their energy efficiency. Building retrofitting is the employed solution for enhancing the energy efficiency in existing buildings. However, the actual performance after retrofitting should be analysed to check the effectiveness of the energy conservation measures. The aim of this work was to detect and to quantify the impact that a retrofitting had in the electrical consumption, heating demands, lighting and temperatures of a building located in the north of Spain. The methodology employed is the application of Functional Data Analyses (FDA) in comparison with classic mathematical techniques such as the Analysis of Variance (ANOVA). The methods that are commonly used for assessing building refurbishment are based on vectorial approaches. The novelty of this work is the application of FDA for assessing the energy performance of renovated buildings. The study proves that more accurate and realistic results are obtained working with correlated datasets than with independently distributed observations of classical methods. Moreover, the electrical savings reached values of more than 70% and the heating demands were reduced more than 15% for all floors in the building.Ministerio de Ciencia, Innovación y Universidades | Ref. RTI2018-096296-B-C2

A Functional Data Analysis for Assessing the Impact of a Retrofitting in the Energy Performance of a Building

There is an increasing interest in reducing the energy consumption in buildings and in improving their energy efficiency. Building retrofitting is the employed solution for enhancing the energy efficiency in existing buildings. However, the actual performance after retrofitting should be analysed to check the effectiveness of the energy conservation measures. The aim of this work was to detect and to quantify the impact that a retrofitting had in the electrical consumption, heating demands, lighting and temperatures of a building located in the north of Spain. The methodology employed is the application of Functional Data Analyses (FDA) in comparison with classic mathematical techniques such as the Analysis of Variance (ANOVA). The methods that are commonly used for assessing building refurbishment are based on vectorial approaches. The novelty of this work is the application of FDA for assessing the energy performance of renovated buildings. The study proves that more accurate and realistic results are obtained working with correlated datasets than with independently distributed observations of classical methods. Moreover, the electrical savings reached values of more than 70% and the heating demands were reduced more than 15% for all floors in the building.This paper was funded by the Spanish Government (Science, Innovation and Universities Ministry) under the project RTI2018-096296-B-C21

IoT-based platform for automated IEQ spatio-temporal analysis in buildings using machine learning techniques

Financiaciado para publicación en acceso aberto: Universidade de Vigo/CISUGProviding accurate information about the indoor environmental quality (IEQ) conditions inside building spaces is essential to assess the comfort levels of their occupants. These values may vary inside the same space, especially for large zones, requiring many sensors to produce a fine-grained representation of the space conditions, which increases hardware installation and maintenance costs. However, sound interpolation techniques may produce accurate values with fewer input points, reducing the number of sensors needed. This work presents a platform to automate this accurate IEQ representation based on a few sensor devices placed across a large building space. A case study is presented in a research centre in Spain using 8 wall-mounted devices and an additional moving device to train a machine learning model. The system yields accurate results for estimations at positions and times never seen before by the trained model, with relative errors between 4% and 10% for the analysed variables.Ministerio de Ciencia, Innovación y Universidades | Ref. RTI2018-096296-B-C2Ministerio de Ciencia, Innovación y Universidades | Ref. FPU17/ 01834Ministerio de Ciencia, Innovación y Universidades | Ref. FPU19/01187Universidad de Vigo | Ref. 00VI 131H 641.0

Análisis del “fouling” procedente de la combustión de pellet de pino y pellet de paja en una caldera de baja potencia: influencia de los parámetros de combustión

El “fouling”, es decir, la formación de depósitos sobre las superficies sometidas principalmente a convección, es uno de los principales problemas en las calderas de combustión de biomasa. La acumulación de estos depósitos puede causar una significante pérdida de eficiencia energética. Su principal causa es la propia composición inorgánica de la biomasa utilizada. Sin embargo, en las calderas de baja potencia, donde los intercambiadores de calor están relativamente cerca de la cámara de combustión, se observa cómo los depósitos están compuestos por una gran parte de material orgánico, existiendo pocos estudios al respecto. Además, no sólo la composición química de la biomasa repercute en la formación de depósitos sino también los parámetros de la combustión, obteniéndose un punto óptimo de operación de la caldera. El estudio realizado consistió en la evaluación de la materia orgánica e inorgánica que componen el “fouling” procedente de la combustión de dos tipos de pellet diferentes, uno de madera de pino y otro de paja, en una caldera de lecho fijo y baja potencia. Primero, se compararon teóricamente ambos tipos de combustibles a través de índices de deposición teóricos. A continuación, se realizaron distintas combustiones variando los siguientes parámetros operativos: la duración de la combustión, la distribución del caudal de aire primario y secundario y el caudal de aire total suministrado. De cada uno de estos ensayos, se recogieron los depósitos del tubo intercambiador de calor. Se distinguieron dos capas; el “fouling adherido”, que se corresponde con la capa más interior pegada al tubo y el “fouling depositado”, que se corresponde con la capa más superficial depositada sobre la anterior. Se observó que ambas capas tenían comportamientos y composiciones ligeramente diferentes. A través de termogravimetría (TG-DSC) se determinó cuantitativamente el contenido de materia orgánica presente en las muestras y su comportamiento térmico. El análisis químico se llevó a cabo usando microscopía electrónica de barrido con espectroscopía de energía dispersiva de rayos X (SEM-EDS) determinando la composición elemental total de cada muestra. Los resultados indicaron que los depósitos tenían una gran cantidad de materia orgánica. Además, en general, en los depósitos de pino, el contenido en materia orgánica de las muestras disminuye cuando la duración de la combustión, el caudal de aire total y el caudal de aire primario aumentan. Lo cual queda corroborado por la cantidad de C obtenido en las muestras con SEM-EDS. Asimismo, se obtuvo una mayor cantidad de Si, sobre todo, pero también de Cl y K, en los depósitos de paja, causando los mayores problemas de combustión experimentados.Los autores agradecen el apoyo financiero del Ministerio de Economía y Competitividad a través del proyecto ENE2012-36405

Automatización del proceso de calibración de modelos térmicos de edificios y sus instalaciones empleando TRNSYS Y GENOPT

El proceso de calibración del modelo térmico de un edificio y sus instalaciones se compone de varios pasos que deben definirse correctamente para poder alcanzar con éxito un resultado coherente y óptimo. Los motores de cálculo como TRNSYS, Energyplus o DOE-2 (entre otros), permiten la definición del modelo del edificio y sus instalaciones a través de archivos de texto, los cuales pueden ser manipulados antes de la ejecución de las simulaciones. Por otro lado, dichos motores pueden ser lanzados mediante órdenes de línea de comandos, lo que constituye una ventaja a la hora de ejecutar procesos por lotes. Con todo esto, la herramienta GENOPT (GENericOPTimizationProgram) enlazada con el software TRNSYS (TRaNsientSYtemsSimulation), se muestra como una alternativa idónea a la hora de realizar el proceso de calibración. En este artículo, se expone una manera de realizar la calibración de edificios y sus instalaciones de forma automatizada, para que el usuario que efectúa el proceso, consiga realizarlo sin conocimientos de la sintaxis que emplea TRNSYS ni de los algoritmos de optimización que implementa GENOPT. El código desarrollado se empleó con éxito en diferentes casos, mostrando la posibilidad de reducir los errores de nuestros modelos utilizando herramientas gráficas accesibles a usuarios no expertosEstá investigación ha sido parcialmente financiada a través del proyecto ITC- 20133033 TERESE3 subvencionado por el CDTI y Fondo Tecnológico -FEDER 2007-2013 Innterconecta apoyado por el Ministerio de Economía y Competitividad y Consejería de Economía e Industria a través Axencia Galega de Innovación (GAIN ) de la Xunta de Galici