The increasing dependency on electricity and demand for renewable energy sources means that distributed system operators face new challenges in their grid. Accurate forecasts of electric load can solve these challenges. In recent years deep neural networks have become increasingly popular in research, and researchers have carried out many experiments to create the most accurate deep learning models

In a deregulated market context, industrial consumers often have multiple market participation options available to bid their flexible consumption in electricity markets and thereby reduce their electricity bill. Yet most participation strategies for demand response are developed in a fixed and predefined set of submarkets. Meanwhile, little literature has compared multiple market options for market

In recent years, the crude oil market has entered a new period of development and the core influence factors of crude oil have also been a change. Thus, we develop a new research framework for core influence factors selection and forecasting. Firstly, this paper assesses and selects core influence factors with the elastic-net regularized generalized linear Model (GLMNET), spike-slab lasso method, and

Worldwide buildings are responsible for about 40% of the overall consumption and contribute to an average of 30% percent of the global carbon emissions. Nevertheless, most current buildings lack efficient energy management systems because such solutions are very expensive, especially when necessary instrumentation needs to be installed after the building’s construction. As an alternative, we purpose

Dear readers, The 1st Energy Informatics. Academy Conference Asia (EI.A Asia 2021) (EnergyInformatics.Academy, 2021a) has collected great contributions from researchers and practitioners in various scientific, technological, engineering and social fields to disseminate original research on the application of digital technology and information management theory and practice to facilitate the global

Limited natural daylight in Nordic Countries means artificial lighting is a critical factor in industrial plant production. The electricity cost of artificial lights accounts for a large percentage of the overall cost of plant production. The optimal use of artificial lighting in plant production can be formulated as a multi-objective problem (MOP) to achieve optimal plant growth while minimizing electricity

The appearance of citizen energy communities demands the conception, development, and testing of new management models for the community and its end-users. Citizen energy communities promote the active participation of end-users, including them in the management of the community. End-users are incentivized to participate in demand response programs and share their energy among peers, enabling a decrease

With the rise in popularity of artificial intelligence, coupled with the growing concern over the environment, there has been a surge in the use of intelligent energy management systems. Additionally, as more buildings transition into the smart grid and, consequently, more energy and environmental data is gathered, there has been a significant increase in the number of data-driven approaches for building

The aim of this article is to describe a novel ICT-centred methodology and software toolchain to enhance the management of a Smart Campus. The project will be implemented at the University of Campinas through a partnership between UNICAMP, CPFL (local Utility Distribution Company) and the University of Southern Denmark. This project was recently submitted to a strategic and priority call from the Brazilian

Model Predictive Control (MPC) can be used in the context of building automation to improve energy efficiency and occupant comfort. Ideally, the MPC algorithm should consider current- and planned usage of the controlled environment along with initial state and weather forecast to plan for optimal comfort and energy efficiency. This implies the need for an MPC application which 1) considers multiple

The formulation and implementation of Low-Carbon City Policy (LCCP) is an essential initiative for China to build its low-carbon society. Based on the panel data of 282 prefecture-level cities in China from 2003 to 2016, this study evaluates the effects of LCCP implementation on pilot cities’ carbon emission performance using difference-in-differences method, and then the mechanism has also been examined

Simulations, especially agent-based simulation, are able to facilitate the investigation of smart energy solutions and business models, and their impacts on the energy system and involved stakeholders. Technical details, alternatives, and multiple options for what-if scenarios influence simulation quality, but no methodology available to support the investigation. This paper proposes a method for identifying

The project aims to create a Greenhouse Industry 4.0 Digital Twin software platform for combining the Industry 4.0 technologies (IoT, AI, Big Data, cloud computing, and Digital Twins) as integrated parts of the greenhouse production systems. The integration provides a new disruptive approach for vertical integration and optimization of the greenhouse production processes to improve energy efficiency

Digitalization is playing an important role in the emerging practice of Building-to-Grid (B2G). However, the majority of the literature only covers either the grid side, the demand side, or the technical aspect of B2G integration, and an overview of the digitalization in B2G and the involved stakeholders is missing. To fill these gaps, this paper proposes a definition of the B2G ecosystem and provides

In recent years, local energy markets have become an important concept in more decentralized energy systems. Implementations in pilot projects provide first insights into different hypotheses and approaches. From a technical perspective, the requirements for the IT infrastructure of a local energy market are diverse, and a holistic view of its architecture is therefore necessary. This article presents

Non-convex scheduling of energy production allows for more complex models that better describe the physical nature of the energy production system. Solutions to non-convex optimization problems can only be guaranteed to be local optima. For this reason, there is a need for methodologies that consistently provide low-cost solutions to the non-convex optimal scheduling problem. In this study, a novel

In the coming years, several transformations in the transport sector are expected, associated with the increase in electric vehicles (EVs). These changes directly impact electrical distribution systems (EDSs), introducing new challenges in their planning and operation. One way to assist in the desired integration of this technology is to allocate EV charging stations (EVCSs). Efforts have been made

Nowadays, society and business rely heavily on Information and Communication Technology (ICT) systems, which are progressing faster than ever. To stay on pace with them, focus is shifted towards integration of individual ICT systems into complex systems, which offers more functionality than simply the sum of individual systems. In this regard, Cyber-Physical Systems (CPSs) have gained significant importance

As the number of photovoltaic (PV) power generators connected to the distribution grid increases, applications of on-load tap changers (OLTCs), power conditioning systems, and static reactive power compensators are being considered to mitigate the problem of voltage violation in low voltage distribution systems. The reactive power control by power conditioning systems and static reactive power compensators

Zheng Ma1, Birte Holst Jørgensen2, Guangchao Chen3, Bo Nørregaard Jørgensen4 1SDU Health Informatics and Technology, the Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark; 2Department of Wind Energy, Society, Market and Policy, Technical University of Denmark, Roskilde Denmark; 3College of Materials Sciences and Opto-Electronic Technology, University of Chinese academy

The current transition towards electric mobility implies that a significant portion of electricity is drawn by and stored in the electric vehicle’s (EV) batteries. Vehicle-to-grid (V2G) technologies can potentially give distribution system operators access to such energy to provide ancillary services, while remunerating the vehicle owners for their availability to participate. Although the benefits

Modeling and simulation have been popularly used for system investigation and evaluation. With proper evaluation, distribution system operators can decide on a reasonable course of action for encouraging energy flexibility and make predictions on the recommended timing and magnitude of system updates under different scenarios. However, there is no efficient tool for system operators to quickly set

Digitalisation potentials in the electricity sector are frequently discussed around the world, especially in Europe which has the largest interconnected continental electricity grid in the world. The analysis and comparison of electricity ecosystems between countries can help to enhance international understanding and cooperation. It can also enable businesses to expand. However, little literature

P1 Welcome message from the organizers Anke Weidlich1, Gunther Gust2, Mirko Schäfer1 1INATECH, University of Freiburg, Emmy-Noether-Str. 2, 79110, Freiburg, Germany; 2Chair for Information Systems Research, University of Freiburg, Rempartstr. 16, 79098, Freiburg, Germany Correspondence: Anke Weidlich (anke.weidlich@inatech.uni-freiburg.de) Dear readers, In this supplement of the proceedings of the

In order to evaluate the effectiveness of innovative services and technologies spanning over the ICT-enabled power system, realistic models are required. Although nowadays there is a wide range of power system data models, these models do not include a lifelike representation of the ICT system architecture and do not consider the level of interconnectedness of the two systems. In this paper, we propose

Nations and companies are forced to reduce CO2 emissions and decelerate global warming. In this development, the transition of the heating sector is still in its infancy despite the relatively large share of thermal energy in the total energy consumption. Industrial companies can contribute significantly to reduce CO2 emissions by using waste heat through connecting their industrial energy supply system

The transport sector is responsible for 25% of global CO2 emissions. To reduce emissions in the EU, a shift from the currently 745,000 operating public buses to electric buses (EBs) is expected in the coming years. Large-scale deployments of EBs and the electrification of bus depots will have a considerable impact on the local electric grid, potentially creating network congestion problems and spikes

Due to a steeply growing number of energy assets, the increasingly decentralized and segmented energy sector fuels the potential for new digital use cases. In this paper, we focus our attention on the application field of asset logging, which addresses the collection, documentation, and usage of relevant asset data for direct or later verification. We identified a number of promising use cases that

Future smart grids can and will be subject of systematic attacks that can result in monetary costs and reduced system stability. These attacks are not necessarily malicious, but can be economically motivated as well. Emerging flexibility markets are of interest here, because they can incite attacks if market design is flawed. The dimension and danger potential of such strategies is still unknown. Automatic

Industrial enterprises represent a significant portion of electricity consumers with the potential of providing demand-side energy flexibility from their production processes and on-site energy assets. Methods are needed for the active and profitable participation of such enterprises in the electricity markets especially with variable prices, where the energy flexibility available in their manufacturing

The ongoing energy transition requires the planning of low-emission municipal energy supply systems. These systems comprise distribution grids for electricity, gas, and heat, as well as energy conversion units such as heating systems. This paper presents a linear optimization model considering these elements in order to identify the cost-minimizing system design while achieving a given CO2 emission

With increasing digitization, new opportunities emerge concerning the availability and use of data in the energy sector. A comprehensive literature review shows an abundance in available unsupervised clustering algorithms as well as internal, relative and external cluster validation indices (cvi) to evaluate the results. Yet, the comparison of different clustering results on the same dataset, executed

Battery electric modeling is a central aspect to improve the battery development process as well as to monitor battery system behavior. Besides conventional physical models, machine learning methods show great potential to learn this task using in-vehicle data. However, the performance of data-driven approaches differs significantly depending on their application and utilized data set. Hence, a comparison

Due to the increasing battery capacity of electric vehicles, European standard electricity socket-outlets at households are not enough for a full charge cycle overnight. Hence, people tend to install (semi-) fast charging wall-boxes (up to 22 kW) which can cause critical peak loads and voltage issues whenever many electric vehicles charge simultaneously in the same area.This paper proposes a centralized

Grey-box modelling combines physical and data-driven models to benefit from their respective advantages. Neural ordinary differential equations (NODEs) offer new possibilities for grey-box modelling, as differential equations given by physical laws and neural networks can be combined in a single modelling framework. This simplifies the simulation and optimization and allows to consider irregularly-sampled

Photovoltaic (PV) systems are considered an important pillar in the energy transition because they are usually located near the consumers. In order to provide accurate PV system models, e.g. for microgrid simulation or hybrid-physical forecast models, it is of high importance to know the underlying PV system parameters, such as location, panel orientation and peak power. In most open PV generation

The transformation of the energy system towards volatile renewable generation, increases the need to manage decentralized flexibilities more efficiently. For this, precise forecasting of uncontrollable electrical load is key. Although there is an abundance of studies presenting innovative individual methods for load forecasting, comprehensive comparisons of popular methods are hard to come across.In

To tackle the climate crisis, the European energy strategy relies on consumers taking ownership of the energy transition, accelerating decarbonisation through investments in low-carbon technologies and ensuring system stability and reliability by actively participating in the market. Therefore, tools are needed to better understand an increasingly complex and actor-dense energy system, tracking socio-technical

Dear readers, The European Union intends to reduce its emissions by 55% in 2030 and aims to be climate neutral in 2050. This makes the integration of intermittent renewable power generation a question of growing importance. Simultaneously, new and potentially flexible loads are added, both large scale with electrolyzers that produce vast amounts of green hydrogen, and small scale, with millions of

Residential solar installations are becoming increasingly popular among homeowners. However, renters and homeowners living in shared buildings cannot go solar as they do not own the shared spaces. Community-owned solar arrays and energy storage have emerged as a solution, which enables ownership even when they do not own the property or roof. However, such community-owned systems do not allow individuals

Due to the complexity of business ecosystems, the architecture of business ecosystems has not been well discussed in the literature, and modeling or simulation of business ecosystems has been rarely focused. Therefore, this paper proposes a business ecosystem ontology and introduces a methodology for business ecosystem architecture design. The proposed methodology includes five stages: 1) Boundary

In a modern smart energy system with increased penetration of renewable energy resources, the amount of data from various sources is growing significantly. Such systems require complex algorithms and controlling on-demand. These requirements can be addressed with on-demand scalability and a stable system. Nowadays, on-demand scalability is achieved by considering cloud computing and Internet of Things

Local energy markets (LEMs) are well suited to address the challenges of the European energy transition movement. They incite investments in renewable energy sources (RES), can improve the integration of RES into the energy system, and empower local communities. However, as electricity is a low involvement good, residential households have neither the expertise nor do they want to put in the time and

With the advent of the smart grid era, the electrical grid is becoming a complex network in which different technologies coexist to bring benefits to both customers and operators. This paper presents a methodology for analyzing Key Performance Indicators (KPIs), providing knowledge about the performance and efficiency of energy systems, focusing on the demand side. In the first stage of the methodology

Distributed generators (D.G.’s) enable us to generate, supply and be self-reliant on power while also allows us to supply power to meet the demand through virtual power plants. The virtual power plants also help us analyse, control, optimise, and help bridge the gap of demand and supply in these vast energy requirements. With this also comes challenges associated with securing physical systems, data

In recent years, it has become increasingly feasible to achieve important improvements of sustainability by integrating sustainable urbanism with smart urbanism thanks to the proven role and synergic potential of data-driven technologies. Indeed, the processes and practices of both of these approaches to urban planning and development are becoming highly responsive to a form of data-driven urbanism

Prudent and meaningful performance evaluation of algorithms is essential for the progression of any research field. In the field of Non-Intrusive Load Monitoring (NILM), performance evaluation can be conducted on real-world aggregate signals, provided by smart energy meters or artificial superpositions of individual load signals (i.e., denoised aggregates). It has long been suspected that testing on

A building management system (BMS) is generally defined as the ‘Brain’ of the building. Building management systems aid in improving occupant comfort and productivity and, enhance the operational efficiency of building energy generation and supply systems. This paper presents the BuildCOM project which is driven by the industry and customer needs. It responds to the increasing demands for energy efficiency

Non-urgent high energy-consuming residential appliances, such as pool pumps, may significantly affect the peak to average ratio (PAR) of energy demand in smart grids. Effective load monitoring is an important step to provide efficient demand response (DR) to PAR. In this paper, we focus on pool pump analytics and present a deep learning framework, PUMPNET, to identify the pool pump operation patterns

Originally proposed as an alternative to traditional energy planning methodology in the 1970s, backcasting is increasingly applied in futures studies related to sustainability, as it is viewed as a natural step in operationalizing sustainable development. This futures study is concerned with data-driven smart sustainable urbanism as an instance of sustainable urban development—a strategic approach

Cyber-attacks these threats, the cyber security assessment of IT and OT infrastructures can foster a higher degree of safety and resilience against cyber-attacks. Therefore, the use of attack simulations based on system architecture models is proposed. To reduce the effort of creating new attack graphs for each system under assessment, domain-specific languages (DSLs) can be employed. DSLs codify the

The IoT and big data technologies have become essential to the functioning of both smart cities and sustainable cities, and thus, urban operational functioning and planning are becoming highly responsive to a form of data-driven urbanism. This offers the prospect of building models of smart sustainable cities functioning in real time from routinely sensed data. This in turn allows to monitor, understand

Power system operation increasingly relies on numerous day-ahead forecasts of local, disaggregated loads such as single buildings, microgrids and small distribution system areas. Various data-driven models can be effective predicting specific time series one-step-ahead. The aim of this work is to investigate the adequacy of neural network methodology for predicting the entire load curve day-ahead and

René Schumann1*, Michael Brand2 and Khoa Nguyen1 1 HES-SO Valais Wallis, SiLab, Rue de Technpole 3, 3960 Sierre, 3960 Sierre, Switzerland; 2 OFFIS - Institut für Informatik, Escherweg 2, 26121 Oldenburg, Switzerland. Correspondence: René Schumann (rene.schumann@hevs.ch) Dear readers, Fig. 1 (abstract P1). General Procedure for Protocol Fuzzing.Full size image Fig. 1 (abstract P4). Developed sensor

Unlocking and managing flexibility is an important contribution to the integration of renewable energy and an efficient and resilient operation of the power system. In this paper, we discuss how the potential of a fleet of battery-electric transportation vehicles can be used to provide frequency containment reserve. To this end, we first examine the use case in detail and then present the system designed

In the context of a pilot project, the Lugaggia Innovation Community (LIC), we address the problem of non-intrusive load monitoring for the purpose of demand side management on low voltage grids in presence of distributed power generation (photovoltaic). From the power readings of smart meters, we estimate the photovoltaic production and detect the activation of major loads (heatpumps and domestic

The transition of the power grid requires new technologies and methodologies, which can only be developed and tested in simulations. Especially larger simulation setups with many levels of detail can become quite slow. Therefore, the number of possible simulation evaluations decreases. One solution to overcome this issue is to use surrogate models, i. e., data-driven approximations of (sub)systems

In this paper, a thorough analysis of quantification of the heating appliances’ flexibility provided by 200 households located in the Sion area (Switzerland) is presented. An extended evaluation of the available flexibility throughout the year as well as a correlation analysis between the outside temperature and flexibility is performed. The influence of pooling households in the prediction process

To face the challenges of climate change, the integration of renewable energy sources in the energy-intensive heating sector is a crucial aspect of emission reduction. For an efficient operation of coupling devices such as heat pumps with intermittent sources of renewable energy, accurate heat load forecasts need to be developed and embedded into an operation strategy to enable further decarbonisation

Heat pumps embody solutions that heat or cool buildings effectively and sustainably, with zero emissions at the place of installation. As they pose significant load on the power grid, knowledge on their existence is crucial for grid operators, e.g., to forecast load and to plan grid operation. Further details, like the thermal reservoir (ground or air source) or the age of a heat pump installation