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Non-Intrusive Load Monitoring and Customer Segmentation assisted demand flexibility provision in Swiss Households
Switzerland is committed to transitioning to a renewable energy system. The Swiss government has set a target of achieving net-zero carbon emissions by 2050. This will require a significant increase in the use of renewable energy sources. The Swiss power grid is also vulnerable to imbalances be-tween supply and demand. Demand flexibility can help to mitigate this risk and ensure the reliable operation of the power grid. Demand flexibility is the ability to shift or reduce energy use in response to changes in sup-ply or price. This is becoming increasingly important as the power grid transitions to renewable energy sources, such as solar and wind power, which are intermittent and less predictable. Demand flexibility can help to balance the grid and reduce the need for expensive and polluting backup power plants. Non-Intrusive Load Monitoring (NILM) and customer segmentation modeling are powerful tools that can be used to develop demand flexibility programs. NILM can be used to identify high-energy-consuming appliances and to track their energy usage over time. Customer segmentation modeling can be used to identify different groups of customers based on their energy consumption patterns. This information can then be used to develop targeted demand flexibility programs that are more likely to be effective for each group of customers.
The proposed project will develop a common database of metering and sub-metering data from Swiss households. This data will be used to develop NILM algorithms and customer segmentation models. The project will also evaluate the demand flexibility potential of Swiss households and develop targeted demand flexibility programs. The project will develop new tools and knowledge that can be used to design and implement effective demand flexibility programs in Switzerland. The project will also contribute to the development of a more reliable and sustainable power system. The key steps envisioned for the completion of the project are as follows:
1. Literature review: A review of the literature on NILM, customer segmentation, and demand flexibility in the context of the Swiss energy sector will be conducted. This will provide a foundation for the development of the proposed framework and database.
2. Data collection and preparation: A common database of metering and sub-metering data from Swiss households will be prepared. The data will be collected from open-source datasets, collaboration partners, and Nest LL. The data will be cleaned, preprocessed, and standardized to ensure consistency and quality.
3. NILM algorithm development and validation: Popular NILM models found in the literature will be tested in the context of Swiss households to disaggregate energy consumption data into individual appliance loads.
4. Customer segmentation modeling: A customer segmentation model will be developed using the NILM-disaggregated data. The model will be used to identify different groups of customers based on their energy consumption patterns.
The proposed project will develop a common database of metering and sub-metering data from Swiss households. This data will be used to develop NILM algorithms and customer segmentation models. The project will also evaluate the demand flexibility potential of Swiss households and develop targeted demand flexibility programs. The project will develop new tools and knowledge that can be used to design and implement effective demand flexibility programs in Switzerland. The project will also contribute to the development of a more reliable and sustainable power system. The key steps envisioned for the completion of the project are as follows: 1. Literature review: A review of the literature on NILM, customer segmentation, and demand flexibility in the context of the Swiss energy sector will be conducted. This will provide a foundation for the development of the proposed framework and database. 2. Data collection and preparation: A common database of metering and sub-metering data from Swiss households will be prepared. The data will be collected from open-source datasets, collaboration partners, and Nest LL. The data will be cleaned, preprocessed, and standardized to ensure consistency and quality. 3. NILM algorithm development and validation: Popular NILM models found in the literature will be tested in the context of Swiss households to disaggregate energy consumption data into individual appliance loads. 4. Customer segmentation modeling: A customer segmentation model will be developed using the NILM-disaggregated data. The model will be used to identify different groups of customers based on their energy consumption patterns.
1. Development of black-box household model: Black-box models for household appliances will be developed using the NILM-disaggregated data and customer segmentation model. The models can be used to simulate and predict energy consumption under different conditions.
2. Demand flexibility analysis (Optional): The impact of demand flexibility on power system operation will be analyzed using the customer segmentation model, NILM-disaggregated data, and occupant comfort criteria.
1. Development of black-box household model: Black-box models for household appliances will be developed using the NILM-disaggregated data and customer segmentation model. The models can be used to simulate and predict energy consumption under different conditions. 2. Demand flexibility analysis (Optional): The impact of demand flexibility on power system operation will be analyzed using the customer segmentation model, NILM-disaggregated data, and occupant comfort criteria.
Dr. Arnab Chatterjee - arnab.chatterjee@empa.ch
Leandro Von Krannichfeldt - leandro.vonkrannichfeldt@empa.ch
Dr. Arnab Chatterjee - arnab.chatterjee@empa.ch Leandro Von Krannichfeldt - leandro.vonkrannichfeldt@empa.ch