This paper proposes the idea to use real-time carbon intensity data to guide residential electricity usage, shifting the focus from price-based incentives to environmental impact. By integrating data from ElectricityMaps.com and applying constraint satisfaction algorithms, the system provides actionable insights to help users reduce their carbon footprint by optimizing electricity consumption based on time slots when the carbon emissions of the national electricity production are low. The proposed system ofers both an API for smart home devices and a user interface for manual control, empowering individuals to make sustainable choices. This approach aims to drive behavioural change by making carbon reduction a central factor in energy consumption decisions.
The urgent need to reduce carbon emissions is now a central challenge in addressing climate change. Among the various sectors contributing to greenhouse gas emissions, electricity generation remains a major source, particularly when it is dependent on fossil fuels. Transitioning to a low-carbon electricity system is critical; however, it is equally important to optimize the way individuals and communities consume electricity. Reducing the carbon emissions associated with electricity usage not only supports national and global climate goals but can also lead to more resilient and eficient energy systems.
Despite widespread awareness of climate change, changing everyday behaviour around electricity
usage remains dificult. One significant barrier is the lack of accessible, real-time insights into the carbon
intensity of the electricity grid. It is generally dificult for people to know when their consumption has a
higher or lower carbon impact. Furthermore, even when information is available, psychological factors
—- such as cognitive overload, which hampers decision-making when individuals are confronted with
complex or excessive information1, habitual behaviors that persist even in the face of good intentions
[
This short paper proposes a novel approach: using advanced algorithms to calculate and visualize the optimal times for electricity use based on real-time and forecasted carbon intensity. By providing clear, actionable insights, this system can empower individuals to make voluntary, informed choices about when to consume electricity in a way that minimizes their carbon footprint. Rather than relying on abstract encouragements to “use less energy”, the proposed solution focuses on making the environmental consequences of daily actions visible, timely, and manageable. This integration of data-driven intelligence and behavioural nudges could significantly enhance the efectiveness of eforts to promote low-carbon living.
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Electricity consumption does not have a constant carbon footprint. The carbon intensity of electricity lfuctuates throughout the day depending on the availability of renewable sources and the use of fossilfuel generation. These dynamics form an important background for the system proposed later in this paper, which seeks to align household electricity usage with periods of lower carbon intensity. This is discussed in Section 2.1.
In addition to these dynamics, it is useful to review existing approaches that have sought to influence electricity consumption patterns. Two widely studied examples are dynamic pricing mechanisms, which encourage residential users to shift consumption in response to fluctuating electricity prices, and industrial load management strategies, such as peak shaving, which redistribute electricity usage to reduce grid strain. Although these approaches are not primarily designed to reduce carbon emissions, they provide valuable insight into the opportunities and limitations of changing consumption behaviour—insights that inform the design of the proposed system.
The carbon intensity of electricity — usually measured in grams of 2-equivalent per kilowatt-hour (g 2eq/kWh) — can vary significantly throughout the day, reflecting the intermittency of renewable generation. Solar production peaks around midday on sunny days, while wind generation is more pronounced during windy periods. In contrast, periods of low renewable output are often balanced by fossil-fuel generation, leading to a higher carbon intensity of electricity consumed.
In Germany, for example, grid carbon intensity has been observed to fluctuate between 100 and
350 g 2eq/kWh over the course of a day, depending on renewable availability and demand levels
[
Tools such as Electricity Maps (app.electricitymaps.com) provide real-time visualization of these
lfuctuations, showing the contribution of each generation source, the resulting 2 emissions, and
the aggregate carbon intensity per country. These platforms rely on the standardized measure of
gCO₂eq/kWh and make the dynamics of grid emissions visible to end-users2 [
Dynamic energy pricing contracts, where electricity prices vary hourly based on real-time market
conditions, have become an increasingly popular tool to influence consumer behaviour [
In industrial and commercial settings, sophisticated analyses of electricity usage patterns are often employed to manage demand more strategically. Techniques such as peak shaving, which reduces electricity consumption during periods of maximum demand, help facilities lower energy costs, reduce the strain on the grid, and prevent net congestion.
These strategies often rely on detailed monitoring and forecasting, enabling facilities to schedule or shift load based on anticipated peaks. For example, in industrial refrigeration, strategic load shift combined with optimized compressor operation sequencing can lead to energy savings of up to 20% compared to traditional control methods [11]. Similarly, in a systematic case study of a food manufacturing facility, the integration of solar PV, battery energy storage, and demand response resulted in reductions of approximately 6.9% in energy costs and 8.6% in 2 emissions [12].
Despite these proven benefits, the technical complexity and infrastructure requirements, such as energy storage systems, smart controls, and forecasting models, limit their applicability in residential settings. These solutions typically require high upfront investments and ongoing operational expertise, making them less accessible to individual households without substantial simplification or automation.
While dynamic pricing encourages residential users to adapt their electricity usage primarily for cost savings, it does not directly address the carbon intensity of electricity consumption. Industrial load management strategies, such as peak shaving, are efective but too complex for household application. Finally, although the dynamics of carbon intensity throughout the day is increasingly visible through platforms such as Electricity Maps, this information has not yet been translated into practical guidance for individual users. To address these gaps, this article proposes a system that leverages smart algorithms to provide simple, actionable information, enabling households to adapt their usage patterns based on carbon intensity rather than price. 1 continuous use (minutes) 2 continuous use (percentage) 3 intermitted use (start time) 4 intermitted use (end time) /lowcarbonhours?horizon=<hours>&duration=<minutes> /lowcarbonhours?horizon=<hours>&usage_perc=<perc> /islowcarbonhour?start=<time>&horizon=<hours>&usage_perc=<perc> /islowcarbonhour?start=<s-time>&end=<e-time>&duration=<minutes>
To develop the system, we first categorize diferent types of electricity usage scenarios. Some appliances, such as dishwashers, require continuous operation for a fixed duration (e.g., 1.5 hours) within a flexible window of time (e.g., the next 12 hours). Other devices, such as heat pumps and refrigerators, operate intermittently, using electricity for short periods (e.g., 10 minutes every half hour) but can tolerate some temporary interruptions without afecting their functionality.
Based on these scenarios, we develop a JSON-based REST API that allows specification of usage constraints, such as “20% runtime within the next 4 hours” or “1.5 hours continuous operation within the next 12 hours.” The system integrates ElectricityMaps.com forecast data on carbon intensity in the coming hours. Using these predictions, it calculates optimal usage patterns that meet the specified constraints and returns them in JSON format, along with the associated estimated carbon emissions.
The REST API provides two resources; both can be used in two diferent ways. Table 1 lists the REST commands.
1. Continuous use (minutes): Can be used for scenarios in which an appliance needs to run for at least <minutes> in the next upcoming <hours> (e.g. a dishwasher). The command returns a time slot that results in the lowest carbon emissions. 2. Continuous use (percentage): Alternative formulation of the scenario above, but allowing for the specification of a duration in <perc>% of the time. 3. Intermitted use with start time: Returns whether it is currently a time slot with low carbon intensity, when an appliance needs to be on within <hours> from <time> for at least <perc>% of the time. This is useful for appliances such as refrigerators 4. Intermitted use with end time: Returns whether it is currently a time slot with low carbon intensity, when an appliance needs to be on for at least <minutes> between <s-time> and <e-time>. This is useful for applications such as charging an electric vehicle.
This API can be used directly by smart household appliances or integrated into home energy management systems such as HomeAssistant3 or Domoticz4. Additionally, the aim is to develop a user-friendly web interface where end-users can manually specify their appliance usage patterns. This interface should provide clear visualizations showing the best times to operate their devices, the total 2 emissions associated with their choices, and the carbon savings achieved by adapting their behaviour.
The proposed system ofers a new pathway for individuals to actively contribute to climate goals by aligning their electricity usage with periods of lower carbon intensity, without sacrificing comfort or convenience. Translating complex data into simple, actionable choices reduces psychological and informational barriers to sustainable behaviour change. 3https://www.home-assistant.io/ 4https://www.domoticz.com/
In the future, the system could evolve to support fully autonomous optimization, where appliances and home energy systems intelligently adapt in real time. Based on measurements of appliance electricity usage, AI algorithms can be used to automatically detect usage patterns and automatically control devices. Furthermore, aggregating anonymized user data could enable broader insight into consumption trends, helping utilities and policymakers design smarter, more sustainable energy infrastructures. Given the extremely high fluctuations in carbon intensity of electricity, empowering large numbers of users with these tools could ultimately drive meaningful reductions in carbon emissions at scale.
At the same time, realizing this potential will require addressing challenges such as interoperability with various household appliances, protecting user privacy, and ensuring that recommendations are considered trustworthy and transparent. If successfully integrated, the system could also complement existing grid management strategies by aligning household flexibility with renewable generation peaks, thus not only reducing emissions but also supporting grid stability. In this way, carbon-aware consumption at the residential level can become a meaningful contributor to the broader transition toward a low-carbon energy system.
Thanks to Zenno Zoomer who implemented some of the algorithms and developed a prototype of dashboard for electricity consumers during his graduation project.
During the preparation of this work, the author used Writefull and GPT‑4o mini in order to do: Grammar and spelling check, Paraphrase and reword, and Improve writing style. After using these tools, the author reviewed and edited the content as needed and takes full responsibility for the content of the publication. [9] M. Hofmann, K. B. Lindberg, Residential demand response and dynamic electricity contracts with hourly prices: A study of norwegian households during the 2021/22 energy crisis, Smart Energy 13 (2024) 100126. [10] R. El-Azab, Smart homes: potentials and challenges, Clean Energy 5 (2021) 302–315. [11] R. Konda, V. Chandan, J. Crossno, B. Pollard, D. Walsh, R. Bohonek, J. R. Marden, Utilizing load shifting for optimal compressor sequencing in industrial refrigeration (2024). URL: https: //arxiv.org/abs/2403.07831. arXiv:2403.07831. [12] M. Fleschutz, M. Bohlayer, M. Braun, M. D. Murphy, From prosumer to flexumer: Case study on the value of flexibility in decarbonizing the multi-energy system of a manufacturing company, Applied Energy 347 (2023) 121430. doi:10.1016/j.apenergy.2023.121430.