Application of artificial immune system to domestic energy management problem

[EN] The connection of devices in a smart home should be done optimally, this helps save energy and money. Numerous optimization models have been applied, they are based on fuzzy logic, linear programming or bio-inspired algorithms. The aim of this work is to solve an energy management problem in a domestic environment by applying an artificial immune system. We carried out a thorough analysis of the different strategies that optimize a domestic environment system, in order to demonstrate the ability of an artificial immune system to find a successful optima that satisfies the problem constraints

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Virtus project: A scalable aggregation platform for the intelligent virtual management of distributed energy resources

open5noVIRTUS Project, funded by Cassa per i Servizi Energetici e Ambientali (Fund for Energy and Environmental Services-CSEA)-Project code CCSEB_00094.The VIRTUS project aims to create a Virtual Power Plant (VPP) prototype coordinating the Distributed Energy Resources (DERs) of the power system and providing services to the system operators and the various players of the electricity markets, with a particular focus on the industrial sector agents. The VPP will be able to manage a significant number of DERs and simulate realistic plants, components, and market data to study different operating conditions and the future impact of the policy changes of the Balancing Markets (BM). This paper describes the project’s aim, the general structure of the proposed framework, and its optimization and simulation modules. Then, we assess the scalability of the optimization module, designed to provide the maximum possible flexibility to the system operators, exploiting the simulation module of the VPP.openBianchi S.; De Filippo A.; Magnani S.; Mosaico G.; Silvestro F.Bianchi S.; De Filippo A.; Magnani S.; Mosaico G.; Silvestro F

Local Short Term Electricity Load Forecasting: Automatic Approaches

Short-Term Load Forecasting (STLF) is a fundamental component in the efficient management of power systems, which has been studied intensively over the past 50 years. The emerging development of smart grid technologies is posing new challenges as well as opportunities to STLF. Load data, collected at higher geographical granularity and frequency through thousands of smart meters, allows us to build a more accurate local load forecasting model, which is essential for local optimization of power load through demand side management. With this paper, we show how several existing approaches for STLF are not applicable on local load forecasting, either because of long training time, unstable optimization process, or sensitivity to hyper-parameters. Accordingly, we select five models suitable for local STFL, which can be trained on different time-series with limited intervention from the user. The experiment, which consists of 40 time-series collected at different locations and aggregation levels, revealed that yearly pattern and temperature information are only useful for high aggregation level STLF. On local STLF task, the modified version of double seasonal Holt-Winter proposed in this paper performs relatively well with only 3 months of training data, compared to more complex methods

Stochastic interval-based optimal offering model for residential energy management systems by household owners

This paper proposes an optimal bidding strategy for autonomous residential energy management systems. This strategy enables the system to manage its domestic energy production and consumption autonomously, and trade energy with the local market through a novel hybrid interval-stochastic optimization method. This work poses a residential energy management problem which consists of two stages: day-ahead and real-time. The uncertainty in electricity price and PV power generation is modeled by interval-based and stochastic scenarios in the day-ahead and real-time transactions between the smart home and local electricity market. Moreover, the implementation of a battery included to provide energy flexibility in the residential system. In this paper, the smart home acts as a price-taker agent in the local market, and it submits its optimal offering and bidding curves to the local market based on the uncertainties of the system. Finally, the performance of the proposed residential energy management system is evaluated according to the impacts of interval optimistic and flexibility coefficients, optimal bidding strategy, and uncertainty modeling. The evaluation has shown that the proposed optimal offering model is effective in making the home system robust and achieves optimal energy transaction. Thus, the results prove that the proposed optimal offering model for the domestic energy management system is more robust than its non-optimal offering model. Moreover, battery flexibility has a positive effect on the system’s total expected profit. With regarding to the bidding strategy, it is not able to impact the smart home’s behavior (as a consumer or producer) in the day-ahead local electricity market.This work is supported by the European Commission H2020 MSCA-RISE-2014: Marie Sklodowska-Curie project DREAM-GO Enabling Demand Response for short and real-time Efficient And Market Based Smart Grid Operation—An intelligent and real-time simulation approach Ref. 641794, and Grant Agreement No. 703689 (Project ADAPT). Moreover, Amin Shokri Gazafroudi acknowledge the support by the Ministry of Education of the Junta de Castilla y León and the European Social Fund through a grant from predoctoral recruitment of research personnel associated with the research project "Arquitectura multiagente para la gestión eficaz de redes de energía a través del uso de técnicas de intelligencia artificial" of the University of Salamanca. Moreover, authors would like to thank Dr. Juan Miguel Morales González from University of Malaga for his thoughtful suggestions.info:eu-repo/semantics/publishedVersio