Machine Learning and GIS Approach for Electrical Load Assessment to Increase Distribution Networks Resilience
Abstract
:1. Introduction
2. Machine Learning for Electrical Load Assessment
2.1. Unsupervised Learning
2.2. Supervised Learning
- Root: the starting point of the tree which contains all the considered samples. The root has only outgoing arrows.
- Nodes: groups of samples created by dividing the samples of a previous node. Nodes have incoming and outgoing arrows.
- Leaves: terminal nodes of the tree, where a decision is finally made. Leaves only have incoming arrows.
- Branch: a smaller tree containing only a part of the whole tree.
- Parent/child: a node is the child of the upper node and the parent of the lower node.
- Pruning: the process of removing a portion of the tree starting from the leaves. It is usually done to avoid overfitting.
3. Proposed Approach
- Dataset creation: we collect georeferenced and power system data, joining them to create the input dataset. Since machine learning techniques are “garbage in–garbage out” approaches, an accurate check of the data must be done at every stage of the process. A clustering using Voronoi polygons [27] is also carried out to assign the georeferenced data to the corresponding secondary substation.
- Best features selection: we consider all the available features estimating the predictor importance to gain information on which features are more promising, impacting the machine learning targets.
- Simulation and testing of machine learning approach: based on the results of the feature selection, a recursive approach is developed, which progressively adds features, testing the performances of three different machine learning approaches: regression tree, least-squares boosting, and random forest. The simulation results are collected and evaluated using different parameters to compare the machine learning methods considered.
3.1. Dataset Creation
3.2. Feature Selection
3.3. Proposed Machine Learning Approach
4. Input Data
4.1. Power System Data
- Subscription power and number of LV customers supplied.
- Number of contracts by type: the customers are divided into 15 clusters among which 5 represent more than 95% of the power contracts.
- Number of users by type: active user, passive user, prosumer.
- Rated power and type of distributed power plants connected to the LV network, i.e., photovoltaic, hydro, thermal, biogas.
4.2. GIS Urban Data
4.3. Environmental Data
5. Simulation Results and Discussion
5.1. Feature Selection
5.2. Stepwise Simulation
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
Appendix A
Feature | Origin |
---|---|
Secondary substation X coordinates (Gauss–Boaga reference system) | Local DSO |
Secondary substation Y coordinates (Gauss–Boaga reference system) | Local DSO |
Secondary substation latitude | Local DSO |
Secondary substation longitude | Local DSO |
Secondary substation transformer size | Local DSO |
Number of users connected to the secondary substation | Local DSO |
Contracted power | Local DSO |
Number of contracts, Type A | Local DSO |
Number of contracts, Type B | Local DSO |
Number of contracts, Type C | Local DSO |
Number of contracts, Type D | Local DSO |
Number of contracts, Type E | Local DSO |
Number of contracts, Type F | Local DSO |
Number of contracts, Type G | Local DSO |
Number of contracts, Type H | Local DSO |
Number of contracts, Type I | Local DSO |
Number of contracts, Type J | Local DSO |
Number of contracts, Type K | Local DSO |
Number of contracts, Type L | Local DSO |
Number of contracts, Type M | Local DSO |
Number of contracts, Type N | Local DSO |
Number of contracts, Type O | Local DSO |
Contracted power, Type A | Local DSO |
Contracted power, Type B | Local DSO |
Contracted power, Type C | Local DSO |
Contracted power, Type D | Local DSO |
Contracted power, Type E | Local DSO |
Contracted power, Type F | Local DSO |
Contracted power, Type G | Local DSO |
Contracted power, Type H | Local DSO |
Contracted power, Type I | Local DSO |
Contracted power, Type J | Local DSO |
Contracted power, Type K | Local DSO |
Contracted power, Type L | Local DSO |
Contracted power, Type M | Local DSO |
Contracted power, Type N | Local DSO |
Contracted power, Type O | Local DSO |
Number of customers | Local DSO |
Off-take energy, passive user | Local DSO |
Off-take energy and release, user that can release | Local DSO |
Off-take energy, EV charging station | Local DSO |
Off-take energy and release, EV charging station | Local DSO |
Off-take energy, EHPs | Local DSO |
Off-take energy, public lights | Local DSO |
Off-take energy, distribution network self-consumption | Local DSO |
Energy production from biogas power plants | Local DSO |
Energy production from PV power plants | Local DSO |
Energy production from hydropower plants | Local DSO |
Energy production from thermal power plants | Local DSO |
Sum of the energy production | Local DSO |
DUSAF, area covered by industrial, commercial, public, military, and private units | GIS [37] |
DUSAF, area covered by airports | GIS [37] |
DUSAF, area covered by arable land (annual crops) | GIS [37] |
DUSAF, Area covered by construction sites | GIS [37] |
DUSAF, area covered by continuous urban fabric (S.L.: >80%) | GIS [37] |
DUSAF, area covered by discontinuous dense urban fabric (S.L.: 50%-80%) | GIS [37] |
DUSAF, area covered by discontinuous low-density urban fabric (S.L.: 10%-30%) | GIS [37] |
DUSAF, area covered by discontinuous medium-density urban fabric (S.L.: 30%-50%) | GIS [37] |
DUSAF, area covered by discontinuous very low-density urban fabric (S.L.: <10%) | GIS [37] |
DUSAF, area covered by fast transit roads and associated land | GIS [37] |
DUSAF, area covered by forests | GIS [37] |
DUSAF, area covered by green urban areas | GIS [37] |
DUSAF, area covered by isolated structures | GIS [37] |
DUSAF, area covered by land without current use | GIS [37] |
DUSAF, area covered by mineral extraction and dump sites | GIS [37] |
DUSAF, area with no data (clouds and shadows) | GIS [37] |
DUSAF, area covered by other roads and associated land | GIS [37] |
DUSAF, area covered by pastures | GIS [37] |
DUSAF, area covered by railways and associated land | GIS [37] |
DUSAF, area covered by sports and leisure facilities | GIS [37] |
DUSAF, area covered by water | GIS [37] |
Number of residents | GIS [36] |
Number of people not residents | GIS [36] |
Number of streets, number of residents | GIS [36] |
Number of streets, number of commercial activities | GIS [36] |
Number of activities | GIS [36] |
NIL | GIS [38] |
Census track ID | GIS [38] |
Residential building volume | GIS [39] |
Commercial building volume | GIS [39] |
Voronoi polygon area | GIS-computed |
Month | Intrinsic |
Day | Intrinsic |
Hour | Intrinsic |
Temperature | Meteo station [43] |
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Feature | R. Tree | Boosting | R. Forest | Origin |
---|---|---|---|---|
Hour | X | X | X | Intrinsic |
Day | X | X | X | Intrinsic |
Temperature | X | X | X | Meteo station [43] |
Contracted power | X | X | X | Local DSO |
Contracted power, Type C | X | X | X | Local DSO |
Contracted power, Type B | X | X | X | Local DSO |
Contracted power, Type E | X | Local DSO | ||
Number of contracts, Type C | X | X | Local DSO | |
Number of contracts, Type B | X | X | Local DSO | |
Number of contracts, Type E | X | Local DSO | ||
Number of contracts, Type D | X | Local DSO | ||
Off-take energy, passive user | X | Local DSO | ||
Off-take energy and release, user that can release | X | X | Local DSO | |
Secondary substation transformer size | X | X | X | Local DSO |
Number of users connected to the secondary sub. | X | X | Local DSO | |
Number of activities | X | X | GIS [36] | |
DUSAF, area covered by other roads and associated land | X | X | GIS [37] | |
Census track ID | X | GIS [38] | ||
Voronoi polygon area | X | GIS-computed | ||
Monthly RMSE on training set [kW] | 11.90 | 37.52 | 13.45 | |
Monthly RMSE [kW] | 51.53 | 42.91 | 41.73 | |
Monthly Relative RMSE [%] | 11.26 | 9.23 | 8.96 |
Algorithm | Index | Working Days | Holidays |
---|---|---|---|
Regression tree | RMSE | 52.55 kW | 49.31 kW |
RMSE% | 11.60% | 10.51% | |
Boosting | RMSE | 44.28 kW | 39.87 kW |
RMSE% | 9.54% | 8.54% | |
Random forest | RMSE | 42.65 kW | 39.73 kW |
RMSE% | 9.20% | 8.44% |
Algorithm | Index | Mon | Tue | Wed | Thu | Fri | Sat | Sun |
---|---|---|---|---|---|---|---|---|
Regression tree | RMSE | 53.38 kW | 53.06 kW | 52.95 kW | 51.89 kW | 51.68 kW | 50.34 kW | 48.26 kW |
RMSE% | 11.68% | 11.68% | 11.65% | 11.46% | 11.54% | 10.65% | 10.37% | |
Boosting | RMSE | 44.90 kW | 45.14 kW | 44.94 kW | 43.71 kW | 42.99 kW | 40.45 kW | 39.28 kW |
RMSE% | 9.67% | 9.65% | 9.61% | 9.44% | 9.38% | 8.56% | 8.52% | |
Random forest | RMSE | 43.26 kW | 43.05 kW | 42.79 kW | 42.17 kW | 42.10 kW | 40.27 kW | 39.18 kW |
RMSE% | 9.31% | 9.26% | 9.20% | 9.12% | 9.15% | 8.51% | 8.36% |
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Bosisio, A.; Moncecchi, M.; Morotti, A.; Merlo, M. Machine Learning and GIS Approach for Electrical Load Assessment to Increase Distribution Networks Resilience. Energies 2021, 14, 4133. https://doi.org/10.3390/en14144133
Bosisio A, Moncecchi M, Morotti A, Merlo M. Machine Learning and GIS Approach for Electrical Load Assessment to Increase Distribution Networks Resilience. Energies. 2021; 14(14):4133. https://doi.org/10.3390/en14144133
Chicago/Turabian StyleBosisio, Alessandro, Matteo Moncecchi, Andrea Morotti, and Marco Merlo. 2021. "Machine Learning and GIS Approach for Electrical Load Assessment to Increase Distribution Networks Resilience" Energies 14, no. 14: 4133. https://doi.org/10.3390/en14144133