Things2People interaction toward energy savings in shared spaces Using BIM

People in shared building space have an important role in energy consumption because they can turn on/off equipment and heat/cooling systems. This behaviour can be influenced by giving then locally tailored context information (energy consumption, temperature, luminosity) and information about the cost of their actions. This paper presents an approach to create personalized local energy consumption predictions in a building using past sensor data, correlated with external conditions to create local context predictions. This prediction is sent in real-time to people’s mobile devices in order to influence their behaviour when increasing or decreasing temperature using heating or cooling systems. This information is essential for sustainability actions in shared spaces, where this information can have an important role. Also, the data (temperature) representation in the building information model (BIM) module can help the user understand environment conditions and, together with the user sharing their thermal feelings, can be used to change behaviour. This approach using BIM’s representation models allows Things2People interaction to improve energy savings in these shared spaces.info:eu-repo/semantics/publishedVersio

Application of Machine Learning to Predict Electricity Demand from Electric Vehicles in Workplace Settings

As sustainability-oriented policies begin to be implemented across the world, adapting the current electric power system (EPS) to meet the demands required by those poli- cies is key to meeting emissions targets. Part of those policies includes the continued expansion of the electrification of national transportation systems. This electrifica- tion of transportation will require the vast expansion of electric vehicle (EV) usage as well as the charging networks that will give them power. The consequent growth in anticipated energy demand must be included in infrastructure planning. As a result, forecasting the charging demand of EVs will be a vital tool to plan for the develop- ment of EPS infrastructure. The identification of the best forecasting methods is a key field of research supporting this effort. This thesis analyzed several statistical models and state-of-the-art (SoA) deep learning (DL) machine learning models to determine relevant forecasting tools for predicting EV charging loads in the context of workplace charging. Workplace charging was identified as a gap in research, where fewer attempts to model EV demand at office buildings and places of work had been recorded. The data set chosen was the NREL workplace charging data set, which included daily charging load from 2017-2020. The time series forecasting models tested include ARIMA, SARIMA, XGBoost, LightGBM, RNN, LSTM, GRU, TFT, and N-BEATS. A machine learning modelling pipeline was developed for each model. Results of modelling determined that the SoA DL models TFT and N-BEATS were the top performing models with a mean average percentage error (MAPE) score of 18.9% and 19.5%, respectively, followed by XGBoost with an MAPE of 21.1%. From a residual error analysis, it was found that TFT poorly estimated peak consumption, but was able to more consistently predict the general trends, as compared to XGBoost and N-BEATS, which performed better with extreme fluctuations, but struggled with non-extreme value

Iberian Energy Market: Spot Price Forecast by Modelling Market Offers

Electricity is a very special commodity since it is economically non-storable, and thus requiring a constant balance between production and consumption. At the corporate level, electricity price forecasts have become a fundamental input to energy companies’ decision making mechanisms [22, 45]. Electric utilities are higly vulnerable to economical crisis, since they generally cannot pass their excess costs on the wholesale market to the retail consumers [77] and, since the price depends on variables like weather (temperature, wind speed, precipitation, etc.) and the intensity of business and everyday activities (on-peak vs. off-peak hours, weekdays vs. weekends, holidays and near-holidays, etc.) it shows specific dynamics not observed in any other market, exhibiting seasonality at the daily, weekly and annual levels, and abrupt, short-lived and generally unanticipated price spikes. These extreme price volatility make price forecasts from a few hours to a few months ahead to become of particular interest to power portfolio managers. An utility company or large industrial consumer who is able to accurately forecast the wholesale prices and it’s volatility, can adjust its bidding strategy and its own production/consumption schedule in order to reduce the risk or maximize the profits in day-ahead trading. In this work I discuss the dynamics of the Iberian electricity day-ahead market (OMIE), review the state-of-the-art forecasting techniques and introduce a new approach to Electricity Price Forecasting, by forecasting the underlying dynamics, the market demand/supply curves. With this method it is possible to predict not only the electricity prices for the next hours, but also the market curves, which can then be used for risk management and a more accurate schedule of generation units. I analyze the model results and benchmark them against other models in the industry.A eletricidade é uma commodity muito especial, uma vez que não é possível armazená-la, e por isso, requer um constante equilíbrio entre a produção e consumo. ao nível empresarial, a previsão de preços de eletricidade tornou-se um input fundamental para os mecanismos de tomada de decisão das companhias [22, 45]. As empresas de eletricidade são altamente vulneráveis a crises económicas, uma vez que, em geral, não conseguem passar os seus custos excessivos para o mercado retalhista [77] e, uma vez que o preço depende de variáveis como meteorologia (temperatura, velocidade do vento, precipitação, etc.) e da intensidade de negócio e das atividades do dia-a-dia (pico vs vazio, dias da semana vs fim-de-semana, feriados e pontes, etc.) apresenta uma dinâmica que não é observada em mais nenhum mercado, com sazonalidade diária, semanal e anual, e com picos de preço abruptos de pouca duração e, em termos gerais, impossíveis de antecipar. Esta volatilidade de preços torna a previsão de preços particularmente interessante para gestores de portfólio, seja a curto ou a longo prazo. Uma companhia de eletricidade ou grande consumidor industrial que seja capaz de prever corretamente os preços do mercado grossista e a sua volatilidade, pode ajustar a estratégia de oferta da sua produção/seu consumo de maneira a reduzir o risco ou maximizar os ganhos no mercado à vista. Neste trabalho abordo a dinâmica do mercado de eletricidade ibérico (Operador de Mercado Iberico - Polo Español (OMIE)), revendo o estado da arte dos métodos de previsão de preços de eletricidade, e introduzo uma nova técnica de previsão de preços de eletricidade, através da previsão da sua dinâmica subjacente, as curvas de mercado da procura e oferta. Com este método é possível prever, não só o preço de eletricidade para as próximas horas, mas também as próprias curvas de oferta, o que pode ser utilizado na gestão de risco ao melhor a capacidade de programar as suas unidades de geração.Os resultados do modelo são analisados e comparados com outros modelos já utilizados na industria

Energy consumption forecasting: a proposed framework

With the development of underdeveloped countries and the digitization of societies, energy consumption is expected to continue to show high growth in the coming decades. While there is still a strong focus on fossil fuels for energy generation, the implementation of energy policies is crucial to gradually shift to renewable sources and the consequent reduction in CO2 emissions. Buildings are currently the sector that consumes the most energy. To contribute for a better energy consumption efficiency, it was proposed a framework, to be applied to buildings or households, to allow users to know their energy consumption and the possibility to forecast it. Different data analysis techniques for time series were used to provide information to the user about their energy consumption as well as to validate important data characteristics, namely stationarity and the existence of seasonality, which can have an impact in the forecasting models. For the definition of the forecasting models, state of the art was done to identify used models for energy consumption forecasting, and three models were tested for both types of data, univariate and multivariate. For the univariate data, the tested models were SARIMA, Holt-Winters and LSTM as for the multivariate data, SARIMA with exogenous variables, Support Vector Regression and LSTM. After the first execution of each model, hyperparameter tuning was done to conclude on the improvement of the results and the robustness of the models for later application to the framework.Com o desenvolvimento de países subdesenvolvidos e a digitalização das sociedades, é esperado que o consumo de energia continue a apresentar um crescimento elevado nas próximas décadas. Existindo ainda um grande foco em fontes fósseis para a geração de energia, a implementação de políticas energéticas são cruciais para a mudança gradual para energias renováveis e consequente redução de emissões de CO2. Edifícios são atualmente o sector que mais energia consomem. De forma a contribuir para uma melhor eficiência no consumo de energia foi proposta uma framework, a aplicar em edifícios ou apartamentos, para possibilitar aos utilizadores ter um conhecimento do seu consumo de energia bem como a previsão desse mesmo consumo. Diferentes técnicas de análise de dados para séries temporais foram utilizadas para proporcionar informação ao utilizador sobre o seu consumo de energia bem como a validação de caraterísticas importantes dos dados, nomeadamente a verificação da estacionariedade e a existência da sazonalidade, que terão impacto no modelo de previsão. Para a definição dos modelos preditivos, foi feita uma revisão de literatura sobre modelos utilizados atualmente para previsão do consumo de energia e testados três modelos para os dois tipos de dados, univariados e multivariados. Para os dados univariados os modelos testados foram SARIMA, Holt-Winters e LSTM e para os dados multivariados SARIMA com variáveis exógenas, Support Vector Regression e LSTM. Após a primeira execução de cada modelo, foi feita uma otimização dos modelos para concluir na melhoria dos resultados previstos e na robustez dos modelos para posterior aplicação na framework

Time Series Modelling

The analysis and modeling of time series is of the utmost importance in various fields of application. This Special Issue is a collection of articles on a wide range of topics, covering stochastic models for time series as well as methods for their analysis, univariate and multivariate time series, real-valued and discrete-valued time series, applications of time series methods to forecasting and statistical process control, and software implementations of methods and models for time series. The proposed approaches and concepts are thoroughly discussed and illustrated with several real-world data examples

Machine Learning for Short-Term Water Demand Predictions

Urban water supply is coming under increased pressure due to urbanisation, water scarcity and climate change. Efficient urban water management can help alleviate this pressure by improving service quality and reducing water loss. Accurate demand and consumption forecasting enables expansion planning, financing, and operation of water distribution systems. Current research often focuses on model-centric approaches where the model is improved to drive forecast accuracy; however, more efficient data usage could be realised as an alternative to model-centric approaches, without incurring additional computation costs. This work investigates the potential of data-centric forecasting approaches, focusing on ways to improve the efficiency of data and computation resource usage for short-term water demand forecasting. To initiate the investigation, several intrinsically different forecasting models are analysed. A total of four different forecasting models, i.e., Prophet, Autoregressive Integrated Moving Average, Neural Network (NN) and Random Forest (RF) are applied to four demand datasets, i.e., one Chinese hourly demand dataset and three UK 15-minute demand datasets. Various aspects of data and model requirements for optimal performance are investigated. Results obtained from the case studies show that prolonging training data may not be necessary, and that accurate sub-daily water demand forecasting is possible with 10 days of past data for model training. In terms of accuracy, neural network and random forest tend to be better suited towards short-term water demand forecasting over statistical models. The second part of the work aims to unbox the four black-box machine learning methods – NN, Long Short-Term Memory (LSTM), RF, Extreme Gradient Boosting (XGB) and explain their inner workings using SHapley Additive exPlanations and Local Interpretable Model-Agnostic Explanations, Prophet and ARIMA are excluded due to inferior forecasting accuracy. Results have found that feature requirement depends on data resolution, the forecasting model used and the forecast time of day. Network-based models (NN and LSTM) are more temporally dependent and feature intensive, indicating that they require more feature inputs to produce equal accuracy compared to tree-based models (RF and XGB). High-resolution forecasts can maintain a high level of accuracy with only one feature at the previous point. The final part of the work analyses the possibility of incorporating Transfer Learning (TL) into the context of water demand forecasting. To evaluate the potential of TL, 18 UK DMAs water demand datasets are used. Experiments are designed to predict water demands in one DMA that has limited or unavailable data, with an aim to anaysing the forecasting ability of models built with alternative DMA data. Results have found that four and eight external DMA datasets are respectively suitable for 15-minute and hourly demand and that limited accuracy gains are achieved from samples size larger than 20,000. Finally, TL-incorporated methods can improve machine learning forecasting accuracy when there is limited data availability. The results obtained in this study prove the usefulness of data-centric approaches’ ability to improve forecasting accuracy. The data-centric approaches explored in this thesis can be used to guide the development of machine learning-based short-term demand forecasting models for researchers, operators, and utilities. Efficient use of forecasting models and demand data holds further potential in improving forecast accuracy, reducing computation cost, and bettering user confidence in the application of machine learning models.EPSR

STOCHASTIC SEASONAL MODELS FOR GLUCOSE PREDICTION IN TYPE 1 DIABETES

[ES] La diabetes es un importante problema de salud mundial, siendo una de las enfermedades no transmisibles más graves después de las enfermedades cardiovasculares, el cáncer y las enfermedades respiratorias crónicas. La prevalencia de la diabetes ha aumentado constantemente en las últimas décadas, especialmente en países de ingresos bajos y medios. Se estima que 425 millones de personas en todo el mundo tenían diabetes en 2017, y para 2045 este número puede aumentar a 629 millones. Alrededor del 10% de las personas con diabetes padecen diabetes tipo 1, caracterizada por una destrucción autoinmune de las células beta en el páncreas, responsables de la secreción de la hormona insulina. Sin insulina, la glucosa plasmática aumenta a niveles nocivos, provocando complicaciones vasculares a largo plazo. Hasta que se encuentre una cura, el manejo de la diabetes depende de los avances tecnológicos para terapias de reemplazo de insulina. Con la llegada de los monitores continuos de glucosa, la tecnología ha evolucionado hacia sistemas automatizados. Acuñados como "páncreas artificial", los dispositivos de control de glucosa en lazo cerrado suponen hoy en día un cambio de juego en el manejo de la diabetes. La investigación en las últimas décadas ha sido intensa, dando lugar al primer sistema comercial a fines de 2017, y muchos más están siendo desarrollados por las principales industrias de dispositivos médicos. Sin embargo, como dispositivo de primera generación, muchos problemas aún permanecen abiertos y nuevos avances tecnológicos conducirán a mejoras del sistema para obtener mejores resultados de control glucémico y reducir la carga del paciente, mejorando significativamente la calidad de vida de las personas con diabetes tipo 1. En el centro de cualquier sistema de páncreas artificial se encuentra la predicción de glucosa, tema abordado en esta tesis. La capacidad de predecir la glucosa a lo largo de un horizonte de predicción dado, y la estimación de las tendencias futuras de glucosa, es la característica más importante de cualquier sistema de páncreas artificial, para poder tomar medidas preventivas que eviten por completo el riesgo para el paciente. La predicción de glucosa puede aparecer como parte del algoritmo de control en sí, como en sistemas basados en técnicas de control predictivo basado en modelo (MPC), o como parte de un sistema de supervisión para evitar episodios de hipoglucemia. Sin embargo, predecir la glucosa es un problema muy desafiante debido a la gran variabilidad inter e intra-sujeto que sufren los pacientes, cuyas fuentes solo se entienden parcialmente. Esto limita las prestaciones predictivas de los modelos, imponiendo horizontes de predicción relativamente cortos, independientemente de la técnica de modelado utilizada (modelos fisiológicos, basados en datos o híbridos). La hipótesis de partida de esta tesis es que la complejidad de la dinámica de la glucosa requiere la capacidad de caracterizar grupos de comportamientos en los datos históricos del paciente que llevan naturalmente al concepto de modelado local. Además, la similitud de las respuestas en un grupo puede aprovecharse aún más para introducir el concepto clásico de estacionalidad en la predicción de glucosa. Como resultado, los modelos locales estacionales están en el centro de esta tesis. Se utilizan varias bases de datos clínicas que incluyen comidas mixtas y ejercicio para demostrar la viabilidad y superioridad de las prestaciones de este enfoque.[CA] La diabetisés un important problema de salut mundial, sent una de les malalties no transmissibles més greus després de les malalties cardiovasculars, el càncer i les malalties respiratòries cròniques. La prevalença de la diabetis ha augmentat constantment en les últimes dècades, especialment en països d'ingressos baixos i mitjans. S'estima que 425 milions de persones a tot el món tenien diabetis en 2017, i per 2045 aquest nombre pot augmentar a 629 milions. Al voltant del 10% de les persones amb diabetis pateixen diabetis tipus 1, caracteritzada per una destrucció autoimmune de les cèl·lules beta en el pàncrees, responsables de la secreció de l'hormona insulina. Sense insulina, la glucosa plasmàtica augmenta a nivells nocius, provocant complicacions vasculars a llarg termini. Fins que es trobi una cura, el maneig de la diabetis depén dels avenços tecnològics per a teràpies de reemplaçament d'insulina. Amb l'arribada dels monitors continus de glucosa, la tecnologia ha evolucionat cap a sistemes automatitzats. Encunyats com "pàncrees artificial", els dispositius de control de glucosa en llaç tancat suposen avui dia un canvi de joc en el maneig de la diabetis. La investigació en les últimes dècades ha estat intensa, donant lloc al primer sistema comercial a finals de 2017, i molts més estan sent desenvolupats per les principals indústries de dispositius mèdics. No obstant això, com a dispositiu de primera generació, molts problemes encara romanen oberts i nous avenços tecnològics conduiran a millores del sistema per obtenir millors resultats de control glucèmic i reduir la càrrega del pacient, millorant significativament la qualitat de vida de les persones amb diabetis tipus 1. Al centre de qualsevol sistema de pàncrees artificial es troba la predicció de glucosa, tema abordat en aquesta tesi. La capacitat de predir la glucosa al llarg d'un horitzó de predicció donat, i l'estimació de les tendències futures de glucosa, és la característica més important de qualsevol sistema de pàncrees artificial, per poder prendre mesures preventives que evitin completament el risc per el pacient. La predicció de glucosa pot aparèixer com a part de l'algoritme de control en si, com en sistemes basats en técniques de control predictiu basat en model (MPC), o com a part d'un sistema de supervisió per evitar episodis d'hipoglucèmia. No obstant això, predir la glucosa és un problema molt desafiant degut a la gran variabilitat inter i intra-subjecte que pateixen els pacients, les fonts només s'entenen parcialment. Això limita les prestacions predictives dels models, imposant horitzons de predicció relativament curts, independentment de la tècnica de modelatge utilitzada (models fisiològics, basats en dades o híbrids). La hipòtesi de partida d'aquesta tesi és que la complexitat de la dinàmica de la glucosa requereix la capacitat de caracteritzar grups de comportaments en les dades històriques del pacient que porten naturalment al concepte de modelatge local. A més, la similitud de les respostes en un grup pot aprofitar-se encara més per introduir el concepte clàssic d'estacionalitat en la predicció de glucosa. Com a resultat, els models locals estacionals estan al centre d'aquesta tesi. S'utilitzen diverses bases de dades clíniques que inclouen menjars mixtes i exercici per demostrar la viabilitat i superioritat de les prestacions d'aquest enfocament.[EN] Diabetes is a significant global health problem, one of the most serious noncommunicable diseases after cardiovascular diseases, cancer and chronic respiratory diseases. Diabetes prevalence has been steadily increasing over the past decades, especially in low- and middle-income countries. It is estimated that 425 million people worldwide had diabetes in 2017, and by 2045 this number may rise to 629 million. About 10% of people with diabetes suffer from type 1 diabetes, characterized by autoimmune destruction of the beta-cells in the pancreas, responsible for the secretion of the hormone insulin. Without insulin, plasma glucose rises to deleterious levels, provoking long-term vascular complications. Until a cure is found, the management of diabetes relies on technological developments for insulin replacement therapies. With the advent of continuous glucose monitors, technology has been evolving towards automated systems. Coined as "artificial pancreas", closed-loop glucose control devices are nowadays a game-changer in diabetes management. Research in the last decades has been intense, yielding a first commercial system in late 2017 and many more are in the pipeline of the main medical devices industry. However, as a first-generation device, many issues still remain open and new technological advancements will lead to system improvements for better glycemic control outputs and reduced patient's burden, improving significantly the quality of life of people with type 1 diabetes. At the core of any artificial pancreas system is glucose prediction, the topic addressed in this thesis. The ability to predict glucose along a given prediction horizon, and estimation of future glucose trends, is the most important feature of any artificial pancreas system, in order to be able to take preventive actions to entirely avoid risk to the patient. Glucose prediction can appear as part of the control algorithm itself, such as in systems based on model predictive control (MPC) techniques, or as part of a monitoring system to avoid hypoglycemic episodes. However, predicting glucose is a very challenging problem due to the large inter- and intra-subject variability that patients suffer, whose sources are only partially understood. These limits models forecasting performance, imposing relatively short prediction horizons, despite the modeling technique used (physiological, data-driven or hybrid approaches). The starting hypothesis of this thesis is that the complexity of glucose dynamics requires the ability to characterize clusters of behaviors in the patient's historical data naturally yielding to the concept of local modeling. Besides, the similarity of responses in a cluster can be further exploited to introduce the classical concept of seasonality into glucose prediction. As a result, seasonal local models are at the core of this thesis. Several clinical databases including mixed meals and exercise are used to demonstrate the feasibility and superiority of the performance of this approach.This work has been supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under the FPI grant BES-2014-069253 and projects DPI2013-46982-C2-1-R and DPI2016-78831-C2-1-R. Moreover, with relation to this grant, a short stay was done at the end of 2017 at the Illinois Institute of Technology, Chicago, United States of America, under the supervision of Prof. Ali Cinar, for four months from 01/09/2017 to 29/12/2017.Montaser Roushdi Ali, E. (2020). STOCHASTIC SEASONAL MODELS FOR GLUCOSE PREDICTION IN TYPE 1 DIABETES [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/136574TESI

The impact of macroeconomic leading indicators on inventory management

Forecasting tactical sales is important for long term decisions such as procurement and informing lower level inventory management decisions. Macroeconomic indicators have been shown to improve the forecast accuracy at tactical level, as these indicators can provide early warnings of changing markets while at the same time tactical sales are sufficiently aggregated to facilitate the identification of useful leading indicators. Past research has shown that we can achieve significant gains by incorporating such information. However, at lower levels, that inventory decisions are taken, this is often not feasible due to the level of noise in the data. To take advantage of macroeconomic leading indicators at this level we need to translate the tactical forecasts into operational level ones. In this research we investigate how to best assimilate top level forecasts that incorporate such exogenous information with bottom level (at Stock Keeping Unit level) extrapolative forecasts. The aim is to demonstrate whether incorporating these variables has a positive impact on bottom level planning and eventually inventory levels. We construct appropriate hierarchies of sales and use that structure to reconcile the forecasts, and in turn the different available information, across levels. We are interested both at the point forecast and the prediction intervals, as the latter inform safety stock decisions. Therefore the contribution of this research is twofold. We investigate the usefulness of macroeconomic leading indicators for SKU level forecasts and alternative ways to estimate the variance of hierarchically reconciled forecasts. We provide evidence using a real case study

Balancing energy in the German market design

Among electricity marketplaces balancing energy takes a central role in mediating the supply and demand equilibrium. This thesis analyzes strategies employed in the balancing energy market, as well as their implications for other marketplaces in the setting of the German electricity market

Developing a SARIMAX model for monthly wind speed forecasting in the UK

Wind is a fluctuating source of energy and, therefore, it can cause several technical impacts. These can be tackled by forecasting wind speed and thus wind power. The introduction of several statistical models in this field of research has brought to light promising results for improving wind speed predictions. However, there is not converging evidence on which is the optimal method. Over the last three decades, significant research has been carried out in the field of short-term forecasting using statistical models though less work focuses on longer timescales. The first part of this work concentrated on long-term wind speed variability over the UK. Two subsets have been used for assessing the variability of wind speed in the UK on both temporal and spatial coverage over a period representative of the expected lifespan of a wind farm. Two wind indices are presented with a calculated standard deviation of 4% . This value reveals that such changes in the average UK wind power capacity factor is equal to 7%. A parallel line of the research reported herein aimed to develop a novel statistical forecasting model for generating monthly mean wind speed predictions. It utilised long-term historic wind speed records from surface stations as well as reanalysis data. The methodology employed a SARIMAX model that incorporated monthly autocorrelation of wind speed and seasonality, and also included exogenous inputs. Four different cases were examined, each of which incorporated different independent variables. The results disclosed a strong association between the independent variables and wind speed showing correlations up to 0.72. Depending on each case, this relationship occurred from 4− up to 12−month lags. The inter comparison revealed an improvement in the forecasting accuracy of the proposed model compared to a similar model that did not take into account exogenous variables. This finding demonstrates the indisputable potential of using a SARIMAX for long-term wind speed forecasting