Monday, October 5, 2026

Going Off-Grid: The Hidden Bill Is Called December

A recent article in the Salzburger Nachrichten tells the story of Klaus Strasser, an electrical engineer who built a house in St. Gilgen with no connection to the public grid at all. His own PV and battery system covers everything: heating (via a ground source heat pump), hot water, and household loads for a home that also serves as his office. It is a genuinely impressive piece of engineering. He put about 100 m² of panels on the roof and the facade (21 kWp in total), designed a custom two-battery setup to get clean 400 V three-phase power out of two normally incompatible battery chemistries, and sized his battery bank to ride out seven to ten days without sun. Eight years in, the system has apparently failed him only once, for 90 minutes.

Reading that made me want to check the numbers for a more ordinary case: a newer, well-insulated but not passive-house-standard home somewhere around Klagenfurt, heated with a heat pump, with an EV added a bit later. Would going off-grid make sense there too, or does Strasser's approach only pay off because he built a genuinely oversized, purpose-designed system from day one? Let's do the math.

The setup

Assume a single-family house built around 2017, decent insulation, heat pump for both space heating and hot water. Annual electricity consumption is 6,500 kWh, mostly concentrated in the colder half of the year because of the heating load. An EV gets added later, bringing annual consumption up to about 8,500 kWh.

The roof comfortably fits a 10 kWp system (part of it is shaded by a tree, but let's not worry about that for now). Using a real Meteonorm-calibrated solar yield simulation for Klagenfurt, that system would produce about 10,953 kWh per year, which is more than enough to cover the 8,500 kWh of annual consumption on paper. So far, so good.

The catch is that annual totals hide the timing problem. Here is the average daily production and consumption per month:

 


In December and January, this 10 kWp system produces only about a third of what the house needs on an average day. From March through October, it produces a comfortable surplus, peaking at nearly two and a half times the daily consumption in June.

Two different reasons for the winter deficit

It's worth pulling apart what actually causes that winter dip, because it's really two separate effects layered on top of each other.

The first is simply shorter days. Less daylight reaching the panels each day, a direct consequence of the Earth's axial tilt, and it happens everywhere that has a real winter. This alone accounts for the dip in the production curve above.

The second is heating demand. Because this is a heat pump house, the electrical consumption side rises in exactly the same months that production is at its weakest, which is the worst possible timing for a system that has to balance daily.

These two effects are related, but maybe not in the way it first looks. It's the reduced solar input of shorter, lower-angle winter days that is the underlying reason it gets cold in the first place, in terms of the surface energy balance. The heating demand is a downstream consequence of that same seasonal solar minimum, and how severe it gets depends on latitude: further from the equator, that minimum is colder.

Which has a nice implication: the heating penalty isn't a fixed law of going off-grid, it's a property of a specific climate and latitude. Move the same house far enough south, or let a few more decades of global warming run their course, and the picture eventually flips: no more heating load in winter, but a cooling load in summer instead, right when PV output is at its peak. At that point the seasonal mismatch would actually shrink rather than grow, since summer cooling demand and summer PV surplus line up nicely, unlike winter heating demand and winter PV deficit. The math in this post is specific to our climate today, not an inherent limitation of PV autonomy as such.

What it would take to close the winter gap with PV alone

If the house is grid-connected, this seasonal mismatch barely matters: the grid absorbs the summer surplus and covers the winter deficit, and net metering or feed-in tariffs settle the difference. But full autonomy, by definition, is not allowed to rely on the grid. So the system has to produce enough on an average December day and an average January day to meet consumption on those days too, not just meet the annual total.

Scaling up (keeping the same roof mix and orientation) to hit that daily balance in the two worst months gives:

Month Consumption/day Production/day at 10 kWp Required system size
January 26.7 kWh 12.1 kWh ≈ 22 kWp
December 26.7 kWh 10.1 kWh ≈ 26.5 kWp

So the array would need to be about 2.6 times larger than the roof-only 10 kWp system, roughly 145 m² of module area (around 58 panels at 460 Wp each). That is already well beyond the 121 m² of usable roof area assumed here, which is exactly the kind of constraint that pushed Strasser toward using vertical facade surfaces in addition to his roof. In our hypothetical case, closing the winter gap the same way would mean covering most of the south, west, and east facades with panels as well, on top of the roof.

And even if enough surface area were available, a 26.5 kWp system built to satisfy December would massively overshoot the rest of the year. At that size, an average June day would yield somewhere around 130 kWh, against a consumption of about 20 kWh. That is roughly 6.5 times more than the house can use on that day. Without a grid connection to sell that surplus into, most of it would simply have to be curtailed, since there is no affordable seasonal storage technology that lets a household charge up in summer and draw it down five months later in winter. Multi-day battery buffering, like the 7 to 10 days Strasser built in, helps with cloudy stretches, but it does nothing for a structural seasonal shortfall that lasts weeks.

So what would actually help?

To be clear, none of this is a knock on what Strasser built. Going fully off-grid is a legitimate and interesting engineering goal, and if you are willing to over-build the PV array, use every available surface including facades, and invest in a serious multi-day battery, it clearly works, his own house is the proof. It is also worth remembering that he never fully gave up the option either: a grid connection was prepared during construction, just never activated.

For a house that does not want to go that far, the more sensible response to this same winter problem is usually not more PV area, but one of two other levers:

  • Stay grid-connected and let the winter deficit be covered by grid draw while the summer surplus gets fed in. Given that the "right-sized" 10 kWp array already overproduces by a factor of about 1.3 annually, there is a reasonable economic case for feeding that surplus back into the grid rather than throwing it away, since the cost of a modest grid connection is easily justified by the value of the exported summer energy.
  • Diversify the heat source for the winter months, for example with a wood or pellet backup, so that the electrical load in December and January drops closer to the summer baseline. This attacks the actual root cause (a heating-driven winter consumption peak) instead of trying to out-build it with panels.

Winter autarky bought purely through PV oversizing is, in most residential cases, the economically weakest of these options. It is a fun problem to size on paper though, and seeing how quickly the required array grows once you demand daily balance in December is a good reminder of just how seasonal solar really is at this latitude.

Wednesday, September 16, 2026

The Austrian Energy Congress Does Not Dance...

...but they are working hard on the architecture of a new European power grid.

More than two centuries ago, in 1814 and 1815, royalty and diplomats gathered in the very same halls of Vienna’s Hofburg and Palais Niederösterreich for the famous Congress of Vienna. Their task was daunting: rebuild European stability and redraw the geopolitical map after years of continental upheaval. History famously quipped that "der Kongress tanzt" (the Congress dances) because of its lavish evening balls, but behind the music, key figures like Metternich and Talleyrand were hammering out systems designed to last for generations.

Fast forward to the Oesterreichs Energie Kongress 2026. The setting remains identical, but the ballroom attire has been swapped for modern engineering precision. Instead of territorial boundaries, today’s delegates are mapping out the politics, responsibilities, resilience, and grid architecture of Europe's energy future. The crisis is no longer post-Napoleonic warfare, but changing geopolitics, climate change, and the mass transition to renewable energy.

Moving Beyond Imperial Ballrooms to Smart Grids

The engineering challenge of our decade mirrors the diplomatic puzzle of 1814: how to build an interconnected, reliable system without causing total collapse under peak stress.

Nowhere is this tension clearer than in transport electrification. Current public and private charging infrastructure can supply less than 44% of the power required for a fully electrified vehicle fleet. To make matters tighter, only about 20% of future battery electric vehicle (BEV) drivers have access to dedicated private parking with personal wallboxes. Most EVs spend their days parked at offices, apartment complexes, and public hubs that lack high-capacity hookups, and traditional grid upgrades are far too slow and expensive to solve the problem in time. For Austria and its neighbors, this is also a question of sovereignty: every kilowatt-hour balanced locally through shared charging and V2G is a kilowatt-hour that doesn't have to be imported, bought at volatile spot-market prices, or routed through congested cross-border lines.

We Are Right There: Presenting Results from Shared Charging

We are on-site at the Hofburg presenting the latest breakthroughs from our Shared Charging initiative, a €9 million project funded by the Climate and Energy Fund and the Austrian Research Promotion Agency (FFG), running from January 2025 through December 2028 under the leadership of go-e GmbH alongside 12 industry and research partners.

Rather than waiting for massive, costly grid overhauls, the Shared Charging ecosystem turns existing constraints into a coordinated advantage:

  • Modular Power Distribution: Smart, semi-public EV charging setups designed to fit directly into current urban workplace and residential infrastructure.
  • Dynamic Energy Balancing: Intelligent algorithms dynamically align charging demand with real-time local supply, soaking up solar generation during sunny hours and wind power during high-wind windows.
  • Bidirectional Charging (V2G): Transforming parked vehicles from passive energy sinks into active storage assets that feed power back into the grid to smooth out peak loads.

Our real-world urban workplace demonstrators prove a crucial point for power systems engineers: preventing grid blackouts in an 80% EV scenario doesn't require tearing up every street in Europe. It simply takes intelligence, coordination, and resource efficiency. . That resilience is measurable: a distributed network of smart chargers and parked EV batteries can absorb local demand spikes or supply drops that would otherwise cascade into brownouts, the same way a diversified alliance system absorbed shocks that a single overstretched empire could not.

While Metternich’s congress waltzed its way toward a 19th-century balance of power, today’s energy community is hard at work balancing megawatt loads. The tunes have changed, but the mission in the Hofburg remains the same: engineering stability for the centuries ahead.

Links:


Wednesday, December 6, 2023

On the Potential of Self-Organizing Energy Systems

In the rapidly evolving field of energy management and autonomous systems, Kristina Wogatai presented her planned dissertation, titled "Exploring the Potential of Self-Organizing Applications in Energy Networks" at the Doctoral Symposium of the 4th IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS 2023). Held from September 25th to 29th in Toronto, Canada, this conference serves as a significant forum for sharing the latest research in autonomous computing, self-adaptation, and self-organization.

Modern society's increasing demands for efficient and sustainable energy management make stable energy supply networks indispensable. However, achieving this stability is challenging due to dynamic environments and diverse constraints from various energy sources. Kristina's research focuses on self-organizing applications as a potential solution to these challenges. These applications enable network components to communicate and collaborate without centralized control, making adaptive decisions to respond to changing conditions.

Inspired by slime molds, Kristina explores their efficient pathways and growth optimization to balance energy demand and load across network components and areas. Her work also addresses the concept of resilience by developing fault-tolerant architectures for energy systems. These architectures incorporate redundant components, alternative pathways, and self-healing mechanisms for network stability, even in the presence of faults or failures.

Additionally, the study explores the integration of nature-inspired approaches with advanced technologies like artificial intelligence to enhance energy grid management. Overall, by focusing on specific research questions and considering the combination of nature-inspired approaches, advanced technologies, and energy grid optimization, this research aims to contribute novel findings and expand the existing body of knowledge in the field of self-organizing applications in energy networks.

Paper

Kristina Wogatai. Exploring the Potential ofSelf-Organizing Applications in Energy Networks. In Proc. IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS), Toronto, Canada, September 25-29, 2023.

Friday, December 1, 2023

Energy Disaggregation with NILM on a Raspberry Pi with Smart-Metering Extension

Our recent work on Energy Disaggregation with Non-Intrusive Load Monitoring (NILM) on a Raspberry Pi with a Smart-Metering Extension was presented at the 2nd International Conference on Power Systems and Electrical Technology (PSET) in Milan, Italy, from August 25th to 27, 2023.

Smart Metering Extension for Raspberry Pi
Non-intrusive load monitoring (NILM) is a promising technology for efficient energy feedback in residential settings, supporting low-cost energy management systems. However, achieving accurate disaggregation necessitates higher sampling frequencies than standard smart meters (15-minute intervals). State-of-the-art methods require a minimum frequency of 1Hz, increasing system costs and privacy concerns. To address this, we propose a cost-effective single-device smart meter utilizing Raspberry Pi and YoMoPie Monitor for efficient and accurate local processing of user data.
Our concept involves a low-cost single-device smart meter that provides direct feedback based on local user data processing. The system’s performance was tested in a laboratory setting under two different scenarios, and promising results were obtained.
Our system demonstrated promising results in disaggregation performance and computational complexity in laboratory tests under two scenarios. This study evaluates implementing NILM on an embedded system with limited resources, achieving satisfactory outcomes for five appliances. The open-source software and hardware enable easy replication and further exploration by the research community and other stakeholders.

Color indicating detected devices by the NILM algorithm




 To learn more, check out the paper

Johannes Winkler, Hafsa Bousbiat, Stefan Jost, and Wilfried Elmenreich. Energy Disaggregation with NILM on a Raspberry Pi with Smart-Metering Extension. In Proc. 2023 2nd International Conference on Power Systems and Electrical Technology (PSET 2023), Milan, Italy, August 25-27, 2023.

or visit our NILM Raspberry Pi project on Github.


Tuesday, June 13, 2023

Unlocking the Full Potential of Neural NILM: On Automation, Hyperparameters & Modular Pipelines

Non-Intrusive Load Monitoring (NILM) is a technique used to monitor the energy usage of individual appliances and devices in a home or building, without the need to physically measure each appliance or device. This allows energy managers to more accurately understand how energy is being used in the building. The basic principle behind NILM is to measure the overall energy usage of the building, and then identify patterns in the usage that can be attributed to specific appliances or devices. By analyzing the total energy usage, NILM can identify the type of appliance and its energy consumption. This information can then be used to make informed decisions about energy management, such as identifying energy-efficient appliances and optimizing energy usage. NILM is important for energy management applications because it provides a more comprehensive view of energy use. By understanding the energy usage of individual devices, energy managers can make better decisions about how to optimize energy usage and reduce energy costs. Furthermore, NILM can identify potential problems in the system, such as inefficient appliances, which can be addressed in order to improve efficiency.

Overview of the NILM pipeline in Deep-NILMTK

In recent years, Non-Intrusive Load Monitoring (NILM) has become an important tool for identifying the power consumption of individual appliances from a single metering point. Deep learning models are gaining traction in this area, however, there are still many challenges surrounding NILM datasets and the lack of common experimental guidelines. This lack of features and best practices guidelines has limited the adoption of efficient research instruments and made it difficult to compare, replicate, and share results.

To address this problem, we have proposed a novel open-source toolkit, Deep-NILMTK, which leverages the best practices for Deep Learning and offers a common testing bed for NILM algorithms. This toolkit includes a modular NILM pipeline that can be easily customised and introduces the concept of Experiment Templating to improve research efficiency. To demonstrate the effectiveness of the tool, we have created an online NILM benchmark repository and conducted a case-study with eight of the most popular deep NILM algorithms. All sources for the tool are available on Github, along with the accompanying documentation.

Leveraging this concept and DL best practices, a case-study of creating an online NILM benchmark repository is provided at https://github.com/BHafsa/DNN-NILM-benchmark considering eight of the most popular deep NILM algorithms. All sources relative to the tool are publicly available on Github https://github.com/BHafsa/deep-nilmtk-v1 along with the corresponding documentation.

Further information can be found in the paper

Hafsa Bousbiat, Anthony Faustine, Christoph Klemenjak, Lucas Pereira, and Wilfried Elmenreich. Unlocking the full potential of neural NILM: On automation, hyperparameters & modular pipelines. IEEE Transactions on Industrial Informatics, pages 1–9, 9 2022. (doi:10.1109/TII.2022.3206322)

 

Monday, April 10, 2023

A New Unobtrusive Activity Monitoring Framework to Age Safely in the Digital Era

In “Ageing Safely in the Digital Era: A New Unobtrusive Activity Monitoring Framework Leveraging on Daily Interactions with Hand-Operated Appliances”,  Hafsa Bousbiat, Gerhard Leitner and Wilfried Elmenreich suggest a new interactive framework to unobtrusively monitor elderlies’ behavior based on their interaction with electrical appliances involved in their daily activities. Due to the extension of the human lifespan, the economy, societal systems, and healthcare services will be affected. For that reason, technologies were developed to counteract these challenges. One of these would be the Non-Intrusive Load Monitoring (NILM) model to generate energy data on the explicit usage of electric devices. This set of techniques employ smart meters to measure the power consumption of different appliances, which indicate daily routines and thus the well-being of the elderly.

Non-intrusive load monitoring (NILM) is a monitoring technology that can infer the energy consumed by individual appliances within a building by analyzing the total energy consumption of the building. This technology dates back to work done by George Hart in the 1990s and has since been developed further. The Smart Grids Group of the Institute of Networked and Embedded Systems has a long-standing experience in developing and using NILM technologies, with their work spanning from fundamental research to practical applications of the technology.

The research work is a collaboration between three institutes  (Digital Age Research Center (D!ARC), Institute of Networked and Embedded Systems (NES), and the Department of Information Systems (ISYS)) at the University of Klagenfurt and is part of the dissertation project of Hafsa Bousbiat, a promising young female researcher who is part of the DECIDE doctoral school. 

Overview of the proposed activity monitoring framework

The paper suggests a new activity monitoring framework based on hand-operated appliances inferred from energy data and discusses two case studies based on their pipeline, including NILM approaches and their effect on activity monitoring. The framework includes a load disaggregation module, an activity monitoring module, and a feedback management module. These modules measure the aggregated power in a household, provide contextual and operational information on the condition of the devices and detect anomalies. It also includes feedback from external agents to overall create a more accurate understanding of recent patterns and routines of the occupants with the help of anomaly detection techniques.

Further information can be found in the paper:

H. Bousbiat, G. Leitner, and W. Elmenreich. Ageing safely in the digital era: A new unobtrusive activity monitoring framework leveraging on daily interactions with hand-operated appliances. Sensors, 22(4), 2022. (doi:10.3390/s22041322)

Thursday, March 9, 2023

Neural NILM Learning Paradigms: From Centralised to Decentralised Learning

Centralised vs collaborative learning
Non-intrusive Load Monitoring (NILM) has become a paramount in both industrial and residential sectors to achieve efficient energy consumption. Deep neural networks have been gaining the highest interest from the research community, commonly referred to as neural NILM. In most cases, neural NILM models follow a centralised based learning scheme, where the energy data is assumed to be available in a central node for training. This practice can, however, raise privacy and security concerns from the consumer’s side since energy data can reveal in-home activities and occupancy records if intercepted. In response, Federated Learning (FL) has been suggested as a viable solution to address these issues. In the paper "Neural NILM Learning Paradigms: From Centralised to Decentralised Learning", an overview of neural NILM models following both a centralised and a federated learning paradigm was presented while also identifying the main challenges with regard to both learning paradigms and potential future research directions for more robust, secure and privacy-preserving models in the neural NILM industry. Overall, as any other new technology, FL has its merits and limitations. Typically, FL provides promising perspectives to solve the privacy issues of energy disaggregation. However, it also opens doors for new challenges, especially those related to the (i) low disaggregation performance of FL-based NILM algorithms, (ii) susceptibility to noise, (iii) lack of labeled sub-metered data at the customer’s level, and (iv) need to adopt robust security mechanisms.

Further information can be found in the paper:

Hafsa Bousbiat, Christoph Klemenjak, Yassine Himeur, Wilfried Elmenreich, Abbes Amira, Wathiq Mansoor, and Shadi Atalla. Neural NILM learning paradigms: From centralised to decentralised learning. In Proceedings of the 2022 5th International Conference on Signal Processing and Information Security (ICSPIS), pages 138–142, December 2022. (doi:10.1109/icspis57063.2022.10002485)

The paper also won the best paper award at the 5th International Conference on Signal Processing and Information Security (ICSPIS) in December 2012.


Thursday, March 2, 2023

The Role of Renewable Energies in the Arctic

Last week, Prof. David Finger from Sustainability Institute and Forum at Reykjavik University visited the University of Klagenfurt as a guest researcher. His inspiring talk at Energy Cluster Meeting XXXI, titled "Climate-Neutral Europe: the Role of Renewable Energies in the Arctic to decarbonize Europe and enhance energy independence", was truly captivating and gave us all a glimpse into the possibilities of an Austrian-Icelandic Energy Cooperation. 

Students and researchers alike were amazed by the future of renewable energy in Europe that Prof. Finger's talk highlighted and left the room with interesting insights into the role of renewable energy in the Arctic. It was a great opportunity to learn more about the progress of climate neutrality and energy independence in Europe and we look forward to further collaborations with Prof. Finger in the future.

Wednesday, March 1, 2023

Energy Informatics 2023 in Vienna -- Call for Papers

The EU aims to be climate-neutral by 2050 – an economy with net-zero greenhouse gas emissions. This objective is at the heart of the European Green Deal and in line with the EU’s commitment to global climate action under the Paris Agreement. The transition to a climate-neutral society is both an urgent challenge and an opportunity to build a better future for all. Energy informatics support in solving many challenges of the energy transition, by providing solutions for intelligent management and operation of energy systems and their assets.

The objective of the DACH+ conference series on Energy Informatics is to promote research, development, and implementation of information and communication technologies in the energy domain and to foster the exchange between academia, industry, and service providers in the German-Austrian-Swiss region and its neighbouring countries (DACH+).

We seek high-quality original contributions addressing the design, adoption, operation and management of smart energy systems, the integration of intermittent renewable generation and energy efficiency gains through ICT, market approaches and mechanisms for ICT-enabled energy systems, and research on associated (decentralised) data-driven decisions. We welcome theoretical contributions as well as publications addressing system design, implementation, and experimentation. The list of topics of interest to the conference includes, but is not limited to:

  • ICT for future energy systems, sector coupling and the integration of intermittent renewable generation
  • Information and decision support systems for future energy markets and mechanisms
  • Energy system modelling and (open) energy system data
  • Protocols and architectures for IT systems in the energy sector
  • Data analytics and machine learning for smart energy systems and decentralised decision-making, as well as platforms for data analysis
  • Open data and software for energy research
  • Management of distributed generation and demand side management
  • ICT for (multi-) energy networks and micro-grids
  • Energy-efficient mobility, charging management for electric vehicles, energy-aware traffic control, and smart grid integration of mobile storage
  • Smart buildings, digital metering, occupant comfort, and user interaction
  • Adoption of ICT in the energy sector
  • Cross-cutting issues including cyber security and privacy protection, interoperability, verification of networked smart grid systems

Posters, Demos and Workshops

Submissions for posters, demos, and workshop suggestions are welcome, too. The topics of interest are the same as indicated above. Posters and demos require the submission of an extended abstract, which will be peer-reviewed. If accepted, the abstract will appear in the conference proceedings. Further details can be found on the conference website.

Submission and Publication

Submitted papers will be reviewed in a double-blind process. Accepted and presented papers will be published in the Springer Open Journal Energy Informatics (https://energyinformatics.springeropen.com). The conference language is English, and papers must be written in English. We solicit full research papers (max. 18 pages of content plus 2 additional pages for references) as well as short papers (max. 10 pages of content plus 2 additional pages for references). Templates and instructions will be made available at http://www.energy-informatics.eu/. Further information on the submission of posters and demos is also available on the website. The Open Access fee for the journal article is included in the registration fee.

Important dates

Apr 09, 2023:                     Submission of papers

May 16, 2023:                     Decision acceptance (assignment of shepherds) / rejection

May – Aug                          Incremental revision process between author and shepherd

Jul 02, 2023:                        Camera-ready deadline for poster abstracts for accepted contributions

Oct 04 2023:                      14th Doctoral Workshop Energy Informatics

Oct 05-06, 2023:                12th DACH+ Conference on Energy Informatics

Thursday, February 23, 2023

Energy to train AI tools, wasted?

Energy used to make and provide online services is an important consideration for many reasons. Production and delivery of online services require energy, and that energy has a direct impact on the environment. The energy used to create and provide online services often comes from burning fossil fuels, such as coal, natural gas, and oil. This burning releases carbon dioxide (CO2) and other pollutants into the atmosphere, contributing to global warming. Burning fossil fuels also releases other harmful pollutants, such as particulate matter, sulfur dioxide, and nitrogen oxides, contributing to air pollution and can cause serious health problems. Increased energy consumption also has a direct effect on our environment. As energy consumption increases, so does the demand for resources such as coal, natural gas, and oil. This can lead to the destruction of ecosystems and habitats, as well as the displacement of communities. Additionally, burning these resources to produce energy contributes to climate change, causing a shift in weather patterns, rising sea levels, and an increase in extreme weather events. The energy used to provide online services also has an impact on the cost of providing these services. The more energy used to power the servers and networks, the more expensive the services become. Additionally, higher energy costs can lead to higher consumer prices, as companies must pass on the extra costs to their customers. Finally, suppose energy used to provide online services is generated from non-renewable sources, such as coal and oil. In that case, it means that the energy used to power these online services will eventually run out, which could negatively impact the availability of these services in the future. Overall, it is essential to consider the energy used to make and provide online services. Burning fossil fuels to power these services contributes to air pollution and global warming while also increasing costs. Additionally, the use of non-renewable resources to generate energy could lead to a decrease in the availability of these services in the future. 

A prominent example of online services is AI chatbots that can provide the user with answers to almost any topic. Other than a search engine that only finds matches of the search text in the indexed documents, AI chatbots can compose new information by drawing connections between the vast amount of information they have been trained with. AI programs like ChatGPT are a highly relevant development because they significantly improve the user experience and enable people from all domains to access sophisticated AI technology. Open AI programs make AI more accessible, allowing developers to share and collaborate on AI models. It also helps reduce development costs and makes integrating AI into existing applications easier. By allowing developers to access and build upon existing models, they can create new and innovative applications that can benefit everyone. Developing AI models helps automate tedious tasks and reduce the time spent on manual labor. By using AI models, businesses can automate mundane tasks and improve their workflow. AI models can also help to improve customer support and increase customer satisfaction. AI models are also important for predicting future trends and predicting customer behavior.

But, despite the fact that users of AI often get free access or a generous free trial, developing and training an AI model does not come for free when we consider the energy budget. The Carbon footprint of training ChatGPT has been estimated to be 1287 MWh [1], in addition to running the services. Are 1287 MWh a number to be concerned with? Probably yes. Is it a number so high that we immediately need to banish AI training for the sake of the environment? I don't think so.

When relating 1287 MWh to a single person, it is a lot. It would mean driving an average European car on fossil fuels for 4,5 Mio km. That is enough to travel the whole road network of the United States or equivalent to the carbon footprint of a flight passenger going form London to New York 320 times.

Nevertheless, ChatGPT has more than one user. In fact, it is one of the fastest-growing online platforms in the world, with around 100 Million users at the time of writing. Dividing the development costs by the users, it amounts to 0.01287 kWh or roughly 1% of the energy required to print a book. 

In other words, if users can utilize the AI system to automate mundane tasks and improve their workflow, the energy spent on creating the AI is probably well-invested. Many usages are recreational, and sometimes the AI provides more fiction than facts, but so is the case with books.

However, we need to keep our eyes open on two issues:

  • The operational cost of running the system: "Cost" would mean here energy cost as well as financial cost. If the system does not work here efficiently, we could end up in a much higher energy waste than 1287 MWh
  • Further developments in training new AIs: competitors might train their own AIs, no matter the (energy) cost. And models are expected to grow in complexity and capabilities, probably also significantly raising the energy required for training a single model.
So let's keep an eye on further developments.

[1] Patterson, D., Gonzalez, J., Hölzle, U., Le, Q., Liang, C., Munguia, L.-M., … Dean, J. (4 2022). The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink. Computer, 55, 18–28. Retrieved from http://arxiv.org/abs/2204.05149

Thursday, January 20, 2022

Energy Informatics 2022 -- Call for Papers

Countries worldwide have strengthened their commitment to decarbonize the energy system at COP26 in Glasgow. This intent necessitates a massive increase in renewable power generation and the electrification of energy demand through sector coupling (e.g., heat and mobility). Over the last year, for example, the DACH countries have created roadmaps to establish hydrogen as an additional sustainable energy carrier. Energy informatics contributes to solving many of the challenges of this energy transition, by connecting various decentralized resources and by operating them intelligently.

The objective of the DACH+ conference series on Energy Informatics is to promote research, development, and implementation of information and communication technologies in the energy domain and to foster the exchange between academia, industry, and service providers in the German-Austrian-Swiss region and its neighbouring countries (DACH+).

We seek high-quality original contributions addressing the design, adoption, operation and management of smart energy systems, the integration of intermittent renewable generation and energy efficiency gains through ICT, market approaches and mechanisms for ICT-enabled energy systems, and research on associated (decentralised) data-driven decisions. We welcome theoretical contributions as well as publications addressing system design, implementation, and experimentation. The list of topics of interest to the conference includes, but is not limited to:

  • ICT for future energy systems, sector coupling and the integration of intermittent renewable generation
  • Information and decision support systems for future energy markets and mechanisms
  • Energy system modelling and (open) energy system data
  • Protocols and architectures for IT systems in the energy sector
  • Data analytics and machine learning for smart energy systems and decentralised decision making, as well as platforms for data analysis
  • Open data and software for energy research
  • Management of distributed generation and demand side management
  • ICT for (multi-) energy networks and micro-grids
  • Energy-efficient mobility, charging management for electric vehicles, energy-aware traffic control, and smart grid integration of mobile storage
  • Smart buildings, digital metering, occupant comfort, and user interaction
  • Adoption of ICT in the energy sector
  • Cross-cutting issues including cyber security and privacy protection, interoperability, verification of networked smart grid systems

Important Date

  • Paper, poster & demo submission: Apr 8, 2022
  • Notification: Jun 6, 2022
  • Camera-ready paper due: Jun 24, 2022
  • Doctoral Workshop: Sep 13-14, 2022
  • Conference: Sep 15-16, 2022

Posters, Demos and Workshop

 
Submissions for posters, demos, and workshop suggestions are welcome, too. Details can be found on the conference website.

Submission and Publication

 
Submitted papers will be reviewed in a double-blind process. Accepted and presented papers will be published in the Springer Open Journal Energy Informatics https://energyinformatics.springeropen.com. The conference language is English, and papers must be written in English. We solicit full research papers (max. 18 pages of content plus 2 additional pages for references) as well as short papers (max. 10 pages of content plus 2 additional pages for references). Templates and instructions will be made available at https://www.energy-informatics.eu/. Further information on the submission of posters and demos is also available on the website. The Open Access fee for the journal article is included in the registration fee.

Tuesday, January 5, 2021

Investigating the impact of data quality on the energy yield forecast using data mining techniques

In this paper, we analysed the impact of using optimum combination of input variables and low dimensional subspace on Photovoltaic (PV) production forecasting accuracy. We worked in collaboration with Prof. Mussetta from Politecnico di Milano.

The main contribution presented in the paper is divided in two parts:
  1. Optimum combination of input meteorological features using feature extraction technique
  2. Low dimensional subspace using dimensional reduction technique
We assess and compare two cases when forecasting models are fed with all the features with the case when low subspace of dataset is used as an input to the models.
The simulation results reveal that depending on the location under study and the regression methods, using less variables as input to the forecasting models are enough to generate nearly similar results without affecting the performance. However, it is necessary to conduct the tests under different climatic conditions so as to ensure the reliability of the results. 
The figures below show the results obtained applying Pearsons correlation and principal component analysis. Figure 1 represents strength of association between two variables. Figure 2 is the biplot representation of the input features contributing variance on principal components PC1 and PC2.

Fig.1 : Pearson correlation map


Fig.2 : Biplot representation 


ISGT Europe 2020 was held virtually and we recorded the presentation for the same. The presentation is available at this link.

 

To support reproducibility and validating the results we have released the dataset utilized in the work along with the codes. 

Github repository with used dataset and evaluation code

For more information please see the paper:

Ekanki Sharma, Marco Mussetta, and Wilfried Elmenreich. Investigating the impact of data quality on the energy yield forecast using data mining techniques. In Proceedings of the IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe). IEEE, October 2020.