Building power demand forecasting using K-nearest neighbours model - practical application in Smart City Demo Aspern project

Following the ongoing transformation of the European power system, in the future, it will be necessary to locally balance the increasing share of decentralised renewable energy supply. Therefore, a reliable short-term load forecast at the level of single buildings is required. In this study, we use a forecaster, which is based on K-nearest neighbours approach and was introduced in an earlier publication, on three buildings of Smart City Demo Aspern project. The authors demonstrate how this forecaster can be applied on different buildings without any manual setup or parametrisation, showing that it is viable to replace load-profiling solutions for predicting electricity consumption at the level of single buildings

Electricity consumption forecasting in office buildings: an artificial intelligence approach

The rising needs for increased energy efficiency and better use of renewable energy sources bring out the necessity for improved energy management and forecasting models. Electricity consumption, in particular, is subject to large variations due to the effect of multiple variables, such as the temperature, luminosity or humidity, and of course, consumers' habits. Current forecasting models are not able to deal adequately with the influence and correlation between the multiple involved variables. Hence, novel, adaptive forecasting models are needed. This paper presents a novel approach based on multiple artificial intelligence-based forecasting algorithms. The considered algorithms are artificial neural networks, support vector machines hybrid fuzzy inference systems, Wang and Mendel's fuzzy rule learning method and a genetic fuzzy system for fuzzy rule learning based on the MOGUL methodology. These algorithms are used to forecast the electricity consumption of a real office building, using multiple input variables and consumption disaggregation.This work has received funding from the European Union's Horizon 2020 research and innovation programme under project DOMINOES (grant agreement No 771066) and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2019.info:eu-repo/semantics/publishedVersio

Uso de algoritmos de aprendizaje automático para analizar datos de energía eléctrica facturada. Caso: Chile 2015 – 2021

In the Chilean electricity market, end users are classified as free customers and regulated customers. The analysis of its behavior is important for the design and application of public policies in the sector. In this research, the monthly billed electricity data of Chilean regulated customers is studied during the 2015-2021 period, to detect patterns and predict the category to which they belong. K-Means algorithms are used for pattern detection, K-NN for customer category prediction, and principal component analysis to determine the most significant variables within the data set. With K-Means it was found that the data is grouped according to the type of client, with K-NN a model was obtained that allows predicting to which type of clients the data belongs, and with the analysis of principal components it was found that the variables customer type, year, and month, are the most important in the data set. More than 96% of the customers analyzed correspond to the residential type, who consumed 50% of the energy invoiced during the study period and imposed the monthly seasonality of the data. The machine learning algorithms applied to the data made it possible to generate models to group them, to predict their category, and to establish the most significant variables in terms of their variance.En el mercado eléctrico chileno, los usuarios finales se clasifican en clientes libres y clientes regulados.  El análisis del consumo energético de los clientes regulados es importante para efectos del diseño y aplicación de las políticas públicas del sector. En esta investigación se hace el análisis de los datos de energía eléctrica facturada mensual de los clientes regulados de Chile, durante el período 2015-2021, con el fin de detectar patrones y predecir la categoría a la que pertenecen. Se utilizan los algoritmos K-Means para la detección de patrones, K-NN para la predicción de la categoría de los clientes, y PCA para determinar las variables más significativas dentro del conjunto de datos. Con K-Means se encontró que los datos se agrupan de acuerdo con el tipo de cliente, con K-NN se obtuvo un modelo que permite predecir a qué tipo de clientes pertenecen los datos, y con PCA se encontró que las variables tipo de cliente, el año y el mes, son las más importantes en el conjunto de datos. Más del 96% de los clientes analizados corresponde al tipo residencial, quienes consumieron el 50% de la energía facturada durante el período de estudio, y además imponen la estacionalidad mensual de los datos. Los resultados obtenidos son de ayuda para el establecimiento y revisión de las políticas aplicadas a los clientes regulados, en cuanto a tarifas, límites de consumo en invierno, y eficiencia energética. Se recomienda continuar la investigación orientándola hacia la predicción del consumo de energía eléctrica

A reduced-dimension feature extraction method to represent retail store electricity profiles

Copyright © 2022 The Author(s). Characterising the inter-seasonal energy performance of buildings is a useful tool for a business to understand what is ‘normal’ for its portfolio of premises and to detect anomalous patterns of energy demand. When adding a new building to the portfolio, it will be useful to predict what will be the likely energy use as part of on-going monitoring of the site. For a large portfolio of buildings with, say, half-hourly energy use measurements (48 dimensions), analysis and prediction will require machine learning tools. Even so, it is advantageous to minimise the amount of data and number of dimensions and features required to find useful patterns in the measurement stream. Our aim is to devise a reduced feature set that can generate a statistically reasonable representation of daily electricity load profiles of retail stores and small supermarkets. We then test if our method is sufficiently accurate to predict and cluster measured patterns of demand. We propose an automatic method to extract features such as times and average demands from electricity load profiles. We used four regression models for prediction and six clustering methods to compare with the results obtained using all of the readings in the load profile. We found that the reduced feature set gave a good representation of the load profile, with only small prediction and clustering errors. The results are robust as prediction is supervised learning and clustering is unsupervised. This simplified feature set is a concise way to represent profiles without using small variances of the demand that do not add useful information to the overall picture. As modern sensor systems increase the volume, availability, and immediacy of data, using reduced dimensional datasets will be key to extracting useful information from high-resolution data streams

AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

In theory, building automation and management systems (BAMSs) can provide all the components and functionalities required for analyzing and operating buildings. However, in reality, these systems can only ensure the control of heating ventilation and air conditioning system systems. Therefore, many other tasks are left to the operator, e.g. evaluating buildings’ performance, detecting abnormal energy consumption, identifying the changes needed to improve efficiency, ensuring the security and privacy of end-users, etc. To that end, there has been a movement for developing artificial intelligence (AI) big data analytic tools as they offer various new and tailor-made solutions that are incredibly appropriate for practical buildings’ management. Typically, they can help the operator in (i) analyzing the tons of connected equipment data; and; (ii) making intelligent, efficient, and on-time decisions to improve the buildings’ performance. This paper presents a comprehensive systematic survey on using AI-big data analytics in BAMSs. It covers various AI-based tasks, e.g. load forecasting, water management, indoor environmental quality monitoring, occupancy detection, etc. The first part of this paper adopts a well-designed taxonomy to overview existing frameworks. A comprehensive review is conducted about different aspects, including the learning process, building environment, computing platforms, and application scenario. Moving on, a critical discussion is performed to identify current challenges. The second part aims at providing the reader with insights into the real-world application of AI-big data analytics. Thus, three case studies that demonstrate the use of AI-big data analytics in BAMSs are presented, focusing on energy anomaly detection in residential and office buildings and energy and performance optimization in sports facilities. Lastly, future directions and valuable recommendations are identified to improve the performance and reliability of BAMSs in intelligent buildings

Data-driven modelling for demand response from large consumer energy assets

Demand response (DR) is one of the integral mechanisms of today’s smart grids. It enables consumer energy assets such as flexible loads, standby generators and storage systems to add value to the grid by providing cost-effective flexibility. With increasing renewable generation and impending electric vehicle deployment, there is a critical need for large volumes of reliable and responsive flexibility through DR. This poses a new challenge for the electricity sector. Smart grid development has resulted in the availability of large amounts of data from different physical segments of the grid such as generation, transmission, distribution and consumption. For instance, smart meter data carrying valuable information is increasingly available from the consumers. Parallel to this, the domain of data analytics and machine learning (ML) is making immense progress. Data-driven modelling based on ML algorithms offers new opportunities to utilise the smart grid data and address the DR challenge. The thesis demonstrates the use of data-driven models for enhancing DR from large consumers such as commercial and industrial (C&I) buildings. A reliable, computationally efficient, cost-effective and deployable data-driven model is developed for large consumer building load estimation. The selection of data pre-processing and model development methods are guided by these design criteria. Based on this model, DR operational tasks such as capacity scheduling, performance evaluation and reliable operation are demonstrated for consumer energy assets such as flexible loads, standby generators and storage systems. Case studies are designed based on the frameworks of ongoing DR programs in different electricity markets. In these contexts, data-driven modelling shows substantial improvement over the conventional models and promises more automation in DR operations. The thesis also conceptualises an emissions-based DR program based on emissions intensity data and consumer load flexibility to demonstrate the use of smart grid data in encouraging renewable energy consumption. Going forward, the thesis advocates data-informed thinking for utilising smart grid data towards solving problems faced by the electricity sector

System organization and operation in the context of local flexibility markets at distribution level

9. Industry, innovation and infrastructur