Efficiency and Optimization of Buildings Energy Consumption: Volume II

This reprint, as a continuation of a previous Special Issue entitled “Efficiency and Optimization of Buildings Energy Consumption”, gives an up-to-date overview of new technologies based on Machine Learning (ML) and Internet of Things (IoT) procedures to improve the mathematical approach of algorithms that allow control systems to be improved with the aim of reducing housing sector energy consumption

Detailed energy analysis of a sheet-metal-forming press from electrical measurements

This paper presents a methodology that allows for the detection of the state of a sheet-metal-forming press, the parts being produced, their cadence, and the energy demand for each unit produced. For this purpose, only electrical measurements are used. The proposed analysis is conducted at the level of the press subsystems: main motor, transfer module, cushion, and auxiliary systems, and is intended to count, classify, and monitor the production of pressed parts. The power data are collected every 20 ms and show cyclic behavior, which is the basis for the presented methodology. A neural network (NN) based on heuristic rules is developed to estimate the press states. Then, the production period is determined from the power data using a least squares method to obtain normalized harmonic coefficients. These are the basis for a second NN dedicated to identifying the parts in production. The global error in estimating the parts being produced is under 1%. The resulting information could be handy in determining relevant information regarding the press behavior, such as energy per part, which is necessary in order to evaluate the energy performance of the press under different production conditions.Xunta de Galicia | Ref. IN854A 2020/0

Improving small power energy estimations for energy audits in offices

Approximately 40% of global energy use can be attributed to buildings; in commercial buildings around 20% of the total energy used comes from small power loads. In the UK,this percentage is expected to reach up to 50% in highly efficient offices in the next 20 years. This trend makes small power loads in commercial buildings one of the fastest growing load categories. Quantitative energy audits for the analysis of the energy performance of buildings are conventionally divided into two approaches, calculation, based on algorithms and equations, and measurement,which performs some level of direct monitoring. The sequantitative energy audit approaches are common tools for evaluating the potential for reducing energy demand in buildings. Small power load estimations in office buildings present challenges for both approaches due to the large number of such loads and their heterogeneous nature, and results in significant uncertainty in these estimations. This thesis investigates the sources of uncertainty of the small power energy estimations for the different audit approaches, and proposes and tests a number of methods and techniques to overcome these weaknesses in the auditing process. For the calculation approach, insufficient input parameter specifications have been identified as the main source of uncertainty, which is associated with variability in the model output. A sensitivity analysis method has been developed to identify the inputs that most contribute to such output variability and that require additional effort to strengthen their accuracy in order to minimize the likely error in calculated small power energy consumption. These influential parameters have been found to depend not only on the information sources available, but also on the calculation method used and the type of load estimated. Regarding the measurement approach, its uncertainty is related to the number of meters used, which increases the quality of the information, but also the complexity of the hardware installation. An extrapolation method for providing the relationship between the number of appliances monitored and the accuracy obtained in the final energy estimations has been proposed. Results showed a logarithmic relationship between the number of desks monitored in a case study office and the relative standard uncertainty percentage obtained in the energy estimations for the aggregated load of the PCs. The method informs about the level of metering infrastructure required in accordance with the level of uncertainty that can be accepted for the small power energy estimations. Non-Intrusive Appliance Load Monitoring (NIALM) methods, as a solution for small power individual load estimation in office buildings, have also been explored through a practical study. The disaggregation capabilities for the different electrical signatures, and their dependence on appliance type and number have been investigated. Although the overall accuracy in the disaggregation process was found to be significantly smaller for offices than for domestic scenarios, some signature combinations, such as the Root Meter Square Increments and the Steady Harmonic Increment, were found to achieve up to 90% of accuracy in the disaggregation process. The outcomes from this study contribute to the extension of the use of existing NIALM methods from domestic to office buildings in the field of small power disaggregation

Proceedings of the 8th International Conference on Energy Efficiency in Domestic Appliances and Lighting

At the EEDAL'15 conference 128 papers dealing with energy consumption and energy efficiency improvements for the residential sector have been presented. Papers focused policies and programmes, technologies and consumer behaviour. Special focus was on standards and labels, demand response and smart meters. All the paper s have been peer reviewed by experts in the sector.JRC.F.7-Renewables and Energy Efficienc

Smart Monitoring and Control in the Future Internet of Things

The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things

Energy consumption in non-domestic buildings based on empirical data

The electricity demand data for a variety of buildings throughout the UK has been made available for analysis. This consists of half hourly resolution data spanning several years for 48 schools (with a mixture of secondary, primary and specialised secondary) and two office buildings, allowing key trends and patterns in energy use to be identified. These trends can include differences between annual profiles, differences between winter and summer months, and differences in weekday and weekend energy use. Additionally, the effect of other variables such as climate, user behaviour and general building data on the building’s energy consumption can be investigated. A database of half hourly school energy demand data, with corresponding building details has been set up and a preliminary analysis preformed. Alternative methods of pattern recognition in non-domestic energy usage are discussed, and the variables necessary to calibrate this information are evaluated. This allowed the possibility of creating ‘generic’ electricity demand profiles for each category of school in each season, leading to a more detailed energy performance benchmark table. Understanding the energy demand, both electricity and gas use, of a building can help the issue of determining how and when energy is used in a day, week, month or year. Only after this knowledge has been gained can energy saving measures be successfully applied and, in turn, can the energy consumption of the non-domestic sector be reduced

Otimização aplicada ao monitoramento não intrusivo de cargas elétricas residenciais

Orientador: Marcos Julio Rider FloresDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Este trabalho apresenta um método de monitoramento não intrusivo (Non-Intrusive Load Monitoring - NILM) baseado em programação linear inteira mista (Mixed-Integer Linear Programming - MILP). NILM são métodos para desagregar leituras de medidores de energia em informações a respeito dos aparelhos eletrodomésticos em operação. Tais informações, como consumo e estado de operação, são valiosas para promover a eficiência energética e manutenção preventiva. A técnica NILM proposta neste trabalho expande o modelo clássico fundamentado em otimização combinatória (Combinatorial Optimization - CO). A nova formulação lida com o problema de ambiguidade de cargas similares, presente no modelo clássico. Restrições lineares são utilizadas para representar eficientemente as assinaturas de carga. Além disso, uma estratégia baseada em janelas temporais é proposta para melhorar o desempenho computacional. A desagregação de cargas pode ser feita utilizando apenas medidas de potência ativa em uma baixa taxa de amostragem, disponível em medidores inteligentes comerciais. A técnica também permite a utilização de outros tipos de medidas, se disponíveis, como a potência reativa. O desempenho do algoritmo é validado utilizando dois casos de teste a partir da base de dados pública AMPds. A taxa de amostragem do caso de teste é de uma amostra por minuto. Os resultados demonstram a habilidade do método proposto para identificar e desagregar com precisão as assinaturas de energia individuais de forma computacionalmente eficienteAbstract: This work presents a non-intrusive load monitoring (NILM) method based on mixed-integer linear programming (MILP). NILM are methods for disaggregating measurements from energy meters into information regarding operating appliances. Such information, such as the power consumption and operating state, are valuable for promoting energy savings and predictive maintenance. The proposed technique expands the classical model based on combinatorial optimization (CO). The new formulation handles the problem of ambiguity of similar loads, present in the classical model. Linear constraints are used to efficiently represent load signatures. Additionally, a window-based strategy is proposed to enhance the computational performance of the proposed NILM algorithm. The disaggregation can be made using only active power measurements at a low sampling rate, which is already available in commercial smart meters. Other features can be added to the model, if available, such as the reactive power. The performance of the algorithm is evaluated using two test cases from the public dataset AMPds. The sampling rate from the test case is of one sample per minute. Results demonstrate the ability of the proposed method to accurately identify and disaggregate individual energy signatures in a computationally efficient wayMestradoEnergia ElétricaMestre em Engenharia Elétric

Demand Response in Smart Grids

The Special Issue “Demand Response in Smart Grids” includes 11 papers on a variety of topics. The success of this Special Issue demonstrates the relevance of demand response programs and events in the operation of power and energy systems at both the distribution level and at the wide power system level. This reprint addresses the design, implementation, and operation of demand response programs, with focus on methods and techniques to achieve an optimized operation as well as on the electricity consumer

Ecosystemic Evolution Feeded by Smart Systems

Information Society is advancing along a route of ecosystemic evolution. ICT and Internet advancements, together with the progression of the systemic approach for enhancement and application of Smart Systems, are grounding such an evolution. The needed approach is therefore expected to evolve by increasingly fitting into the basic requirements of a significant general enhancement of human and social well-being, within all spheres of life (public, private, professional). This implies enhancing and exploiting the net-living virtual space, to make it a virtuous beneficial integration of the real-life space. Meanwhile, contextual evolution of smart cities is aiming at strongly empowering that ecosystemic approach by enhancing and diffusing net-living benefits over our own lived territory, while also incisively targeting a new stable socio-economic local development, according to social, ecological, and economic sustainability requirements. This territorial focus matches with a new glocal vision, which enables a more effective diffusion of benefits in terms of well-being, thus moderating the current global vision primarily fed by a global-scale market development view. Basic technological advancements have thus to be pursued at the system-level. They include system architecting for virtualization of functions, data integration and sharing, flexible basic service composition, and end-service personalization viability, for the operation and interoperation of smart systems, supporting effective net-living advancements in all application fields. Increasing and basically mandatory importance must also be increasingly reserved for human–technical and social–technical factors, as well as to the associated need of empowering the cross-disciplinary approach for related research and innovation. The prospected eco-systemic impact also implies a social pro-active participation, as well as coping with possible negative effects of net-living in terms of social exclusion and isolation, which require incisive actions for a conformal socio-cultural development. In this concern, speed, continuity, and expected long-term duration of innovation processes, pushed by basic technological advancements, make ecosystemic requirements stricter. This evolution requires also a new approach, targeting development of the needed basic and vocational education for net-living, which is to be considered as an engine for the development of the related ‘new living know-how’, as well as of the conformal ‘new making know-how’