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

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

Modelado e optimización mediante técnicas de aprendizaxe automático en edificios con instalacións renovables de produción, almacenamento e consumo eléctricos

The building sector was designed for situations, needs and ways of life different from the current ones, without sufficient consideration of the climatic conditions. The energy needs in this sector represent the largest share of final energy use worldwide, corresponding to a large rate of greenhouse gas emissions. In addition, the demand for energy in the building sector is growing annually and this trend is expected to continue in the coming years. Facing current needs inevitably requires a significant improvement in the energy efficiency of built heritage. Therefore, despite the increase in the population and the global energy demand that is expected for 2040, an improvement in energy efficiency in buildings can reduce more than 40% of emissions, which allows us to be online. with the sustainable development goals. Energy management is one of the key pillars in obtaining the sustainable development goals set by the United Nations. In this way, it is necessary to study the synergies and establish reliable prediction techniques in conjunction with a management system to obtain accurate and efficient building modeling. This project intends to use machine learning (ML) and deep learning (DL) techniques to model and predict the loads and photovoltaic (PV) production of buildings because their accuracy in modeling complex and non-linear patterns has been demonstrated. To improve the energy use of buildings, an intelligent energy management system (IEMS) is considered based on the ML and DL predictions. Balancing is done according to power needs with mixed integer linear programming (MILP) and deep reinforcement learning (DRL) methods. Artificial intelligence (AI) allows you to quickly adapt to information in real time, at low cost, based on the recognition of complex patterns. Thus, the focus on AI allows decisions to be made in real time in the storage system, choosing the best option to manage energy in buildings, that is, optimizing its management. The interpretation of this information to make the best decisions in real time with minimal human intervention can be carried out by an IEMS. With AI, the IEMS demonstrates a high degree of success in energy control and management. This provides the intelligence to the system. Based on the predictions with ML and DL, the IEMS selects the optimal action at each instant by means of MILP or DRL. This combination of techniques allows the storage system to consider current and future demands. Furthermore, as time increases, so does the information, allowing the techniques being submitted for the IEMS to learn and adjust more precisely to potential power fluctuations.El sector edificación fue diseñado para situaciones, necesidades y formas de vida diferentes a las actuales, sin suficiente consideración de las condiciones climáticas. Las necesidades energéticas en este sector representan la mayor cuota del uso final de energía a nivel mundial, correspondiendo a una amplia tasa de las emisiones de gases de efecto invernadero. Además, la demanda de energía en el sector edificación está creciendo anualmente y se espera que esta tendencia se mantenga en los próximos años. Hacer frente a las necesidades actuales requiere, inevitablemente, una mejora significativa de la eficiencia energética del patrimonio construido. Por lo tanto, a pesar del incremento de la población y de la demanda energética a nivel global que está prevista para 2040, una mejora en la eficiencia energética en los edificios puede reducir más del 40% de las emisiones, lo que permite estar en línea con los objetivos de desarrollo sostenible. La gestión de energía es uno de los pilares clave en la obtención de los objetivos de desarrollo sostenible marcados por las Naciones Unidas. De esta manera, es necesario estudiar las sinergias y establecer técnicas de predicción fiables en conjunto con un sistema de gestión con el fin de obtener modelados de edificios precisos y eficientes. En este proyecto se pretende utilizar técnicas de aprendiza automático (ML) y aprendizaje profundo (DL) para modelar y predecir las cargas y la producción fotovoltaica (PV) de los edificios porque está demostrada su precisión en el modelado de patrones complejos y no lineales. Con el fin de mejorar el uso de la energía de los edificios se considera un sistema inteligente de gestión de la energía (IEMS) a partir de las predicciones de ML y DL. El balance se realiza de acuerdo con las necesidades energéticas con métodos de programación lineal mixta (MILP) y de aprendizaje por refuerzo profundo (DRL). La inteligencia artificial (AI) permite adaptarse rápidamente a la información en tiempo real, con un bajo coste, a partir del reconocimiento de patrones complejos. Así, el enfoque en la AI permite tomar decisiones en tiempo real en el sistema de almacenamiento, escogiendo la mejor opción para gestionar la energía en los edificios, es decir, optimizando la gestión de esta. La interpretación de esta información para tomar las mejores decisiones en tiempo real con mínima intervención humana puede ser llevada a cabo por un IEMS. Con la AI, el IEMS demuestra un elevado grado de éxito en el control y gestión de la energía. Esta, proporciona la inteligencia al sistema. Basándose en las predicciones con ML y DL, el IEMS selecciona la acción óptima en cada instante por medio de MILP o DRL. Esta combinación de técnicas permite al sistema de almacenamiento considerar las demandas actuales y futuras. Además, a medida que el tiempo aumenta, la información también lo hace, permitiendo a las técnicas que se presentan para el IEMS aprender y ajustarse con mayor precisión a las posibles fluctuaciones de energía.O sector edificación foi deseñado para situacións, necesidades e formas de vida diferentes as actuais, sen suficiente consideración das condicións climáticas. As necesidades enerxéticas neste sector representan a maior cota do uso final de enerxía a nivel mundial, correspondendo una ampla tasa das emisións de gases de efecto invernadoiro. Ademais, a demanda de enerxía no sector edificación está crecendo anualmente e espérase que esta tendencia se manteña nos próximos anos. Combater as necesidades actuais require, inevitablemente, unha mellora significativa da eficiencia enerxética do patrimonio construído. Polo tanto, a pesar do incremento da poboación e da demanda enerxética a nivel global que se prevé para 2040, unha mellora na eficiencia enerxética nos edificios pode reducir máis do 40% das emisións, o que permite estar en liña cos obxectivos de de desonvemento sostible. A xestión da enerxía é un dos pilares clave na obtención dos obxectivos de desenvolvemento sostible marcados polas Nacións Unidas. Desta maneira, é necesario estudar as sinerxías e establecer técnicas de predición fiables en conxunto cun sistema de xestión co fin de obter modelados de edificios precisos e eficientes. Neste proxecto preténdese utilizar técnicas de aprendizaxe automático (ML) e aprendizaxe profundo (DL) para modelar e predicir as cargas e a produción fotovoltaica (PV) dos edificios porque está demostrada a súa precisión na modelaxe de patróns complexos e non lineais. Co fin de mellorar o uso da enerxía dos edificios é considerado un sistema intelixente de xestión da enerxía (IEMS) a partir das predicións de ML e DL. O balance realízase de acordo coas necesidades enerxéticas con métodos de programación lineal mixta (MILP) e de aprendizaxe por reforzo profundo (DRL). A intelixencia artificial (AI) permite adaptarse rapidamente á información en tempo real, cun coste baixo, a partir do recoñecemento de patróns complexos. Así, o enfoque na AI permite tomar decisións en tempo real no sistema de almacenaxe, escollendo a mellor opción para xestionar a enerxía nos edificios, é dicir, optimizando a xestión da mesma. A interpretación desta información para tomar as mellores decisións en tempo real con mínima intervención humana pode ser levada a cabo por un IEMS. Coa AI, o IEMS demostra un elevado grado de éxito no control e xestión de enerxía. Ésta, proporciona a intelixencia ao sistema. Baseándose nas predicións con DL e ML, o IEMS selecciona a acción óptima en cada instante por medio de MILP ou DRL. Esta combinación de técnicas permite ao sistema de almacenaxe considerar demandas actuais e futuras. Ademais, a medida que o tempo aumenta, a información tamén o fai, permitindo ás técnicas que se presentan para o IEMS aprender e axustarse con maior precisión ás posibles flutuacións de enerxía

Optimization of the electrical demand of an existing building with storage management through machine learning techniques

Accurate prediction from electricity demand models is helpful in controlling and optimizing building energy performance. The application of machine learning techniques to adjust the electrical consumption of buildings has been a growing trend in recent years. Battery management systems through the machine learning models allow a control of the supply, adapting the building demand to the possible changes that take place during the day, increasing the users’ comfort, and ensuring greenhouse gas emission reduction and an economic benefit. Thus, an intelligent system that defines whether the storage system should be charged according to the electrical needs of that moment and the prediction of the subsequent periods of time is defined. Favoring consumption in the building in periods when energy prices are cheaper or the renewable origin is preferable. The aim of this study was to obtain a building electrical energy demand model in order to be combined with storage devices with the purpose of reducing electricity expenses. Specifically, multilayer perceptron neural network models were applied, and the battery usage optimization is obtained through mathematical modelling. This approach was applied to a public office building located in Bangkok, Thailand

Towards DC energy efficient homes

The aim of this paper is to shed light on the question regarding whether the integration of an electric battery as a part of a domestic installation may increase its energy efficiency in comparison with a conventional case. When a battery is included in such an installation, two types of electrical conversion must be considered, i.e., AC/DC and DC/AC, and hence the corresponding losses due to these converters must not be forgotten when performing the analysis. The efficiency of the whole system can be increased if one of the mentioned converters is avoided or simply when its dimensioning is reduced. Possible ways to achieve this goal can be: to use electric vehicles as DC suppliers, the use of as many DC home devices as possible, and LED lighting or charging devices based on renewables. With all this in mind, several scenarios are proposed here in order to have a look at all possibilities concerning AC and DC powering. With the aim of checking these scenarios using real data, a case study is analyzed by operating with electricity consumption mean values

Simulation of wind speeds with spatio-temporal correlation

Nowadays, there is a growing trend to incorporate renewables in electrical power systems and, in particular, wind energy, which has become an important primary source in the electricity mix of many countries, where wind farms have been proliferating in recent years. This circumstance makes it particularly interesting to understand wind behavior because generated power depends on it. In this paper, a method is proposed to synthetically generate sequences of wind speed values satisfying two important constraints. The first consists of fitting the given statistical distributions, as the generally accepted fact is assumed that the measured wind speed in a location follows a certain distribution. The second consists of imposing spatial and temporal correlations among the simulated wind speed sequences. The method was successfully checked under different scenarios, depending on variables, such as the number of locations, the duration of the data collection period or the size of the simulated series, and the results were of high accuracy

Towards DC Energy Efficient Homes

The aim of this paper is to shed light on the question regarding whether the integration of an electric battery as a part of a domestic installation may increase its energy efficiency in comparison with a conventional case. When a battery is included in such an installation, two types of electrical conversion must be considered, i.e., AC/DC and DC/AC, and hence the corresponding losses due to these converters must not be forgotten when performing the analysis. The efficiency of the whole system can be increased if one of the mentioned converters is avoided or simply when its dimensioning is reduced. Possible ways to achieve this goal can be: to use electric vehicles as DC suppliers, the use of as many DC home devices as possible, and LED lighting or charging devices based on renewables. With all this in mind, several scenarios are proposed here in order to have a look at all possibilities concerning AC and DC powering. With the aim of checking these scenarios using real data, a case study is analyzed by operating with electricity consumption mean values