Machine Learning for Identifying Demand Patterns of Home Energy Management Systems with Dynamic Electricity Pricing
Abstract
:1. Introduction
2. Design of Natural Experiment
3. Results
3.1. Panel Data Analysis of Dynamic Pricing Schemes
3.2. Reduced Dimension Clustering to Identify Demand Patterns
- ‘Cluster 1’ (18.09) represents 17% of the participating households. The group of households consists of large groups of people, that are only residing in urban or suburban areas. The majority of this group resides in semi-detached and row-houses. The group represents higher-end urban workers with families.
- ‘Cluster 2’ (15.75) represents the largest group of households with 36%. This group represents households living in urban areas, in row or semi-detached houses. The group represents lower-end urban workers.
- ‘Cluster 3’ (14.07) represents 21% of the participating households. The cluster primarily contains three-person households, which live mostly in detached houses, in the city or rural areas. We identify young starters to be present in this group.
- ‘Cluster 4’ (4.59) is characterized by a small group composition of 2 persons on average. They are represented in all classes of building type and terrain, but with a relatively large size of building. We find this group to consist of mainly seniors.
- ‘Cluster 5’ (31.81) is only comprised of 2 households, relatively small in number of occupants, living in detached houses. As the cluster is not found to be significant, it will be ignored for the following elaborations.
4. Conclusions
Acknowledgment
Author Contributions
Conflicts of Interest
Appendix A
Term | Abreviation | Description |
---|---|---|
APX Energy Market | APX | The Dutch spot wholesale electricity market. |
Real-Time Price | RTP | Type of dynamic pricing tariff, in which customers are informed about, mostly hourly changing, varying electricity prices on a day-ahead or hour-ahead basis [11]. |
Time-of-Use Price | TOU | Type of Dynamic Pricing, in which prices are changing during specific time periods at a fixed rate. |
Energy Management System | EMS | Hardware system in households that enables monitoring of energy usage and communication between utility providers and households. |
Demand-Side Management | DSM | Demand side management strategies, such as demand response, aim to engage consumers of electrical energy, in order to enhance demand flexibility. |
Demand Response | DR | Demand Response (DR) programs offer financial incentives to take actions to reduce or shift load in correspondence to market price behavior. |
Incentive-Based Program | IBP | In IBP programs, consumers are offered non-monetary incentives to alter their electricity consumption behavior. |
Price-Based Program | PBP | In PBP programs, consumers are offered dynamic pricing rates over time, typically with prices significantly higher during peak-periods than during off-peak periods. |
Variable | Symbol | Description |
---|---|---|
Number of Household Occupants | The number of persons permanently occupying a household. | |
Building Size | The size of a households’ building in square meters. | |
Building Age | The building age is taken into consideration as a categorical variable with four general categories: 27 years or younger Between 40 and 28 years Between 50 and 41 years 51 years or older | |
Building Type | The building type a household is residing in, partitioned into five categories. The values of this categorical variable range from one to five. Apartment (‘Appartement’) Row House (‘Tussenwoning’) Detached House (‘Vrijstaand’) Corner House (‘Hoekwoning’) Semi-detached House (‘Twee onder een kap’) | |
Terrain Type | Terrain Type is a categorical variable that indicates whether a household is located in an ‘Urban’, ‘Suburban’, or ‘Rural’ area. The categorization benchmark was set as follows: Rural—less than 1000 inhabitants per km2 at household location Suburban—between 1000 and 3000 inhabitants per km2 at household location Urban—more than 3000 inhabitants per km2 at household location | |
PV Panel Ownership | PV panel ownership is included as a dummy variable, indicating 1 as ownership and 0 as non-ownership. | |
Ventilation Type | Attributed for by a categorical variable. Houses in our study either have ‘Natural’ or ‘Mechanical’ Ventilation. | |
Roof Insulation | Availability of roof insulation is included as a dummy variable, indicating 1 as available and 0 as not available. | |
Solar Heating | Availability of solar heating is included as a dummy variable, indicating 1 as available and 0 as not available. | |
Solar Influx | Potential PV production amount and sunshine intensity measured as the Solar influx in J/cm2. Solar influx was measured as hourly data from the Ministry of Climatology of the Netherlands (KNMI). Moreover, solar influx information was taken from 20 different weather stations. Each household received the solar influx information from the weather station closest to the household. | |
Time-of-Use Price (TOU) | The TOU electricity prices are composed of a marginal fee of the electricity provider that reflects the forward price and a risk premium. The TOU electricity price changes between peak- and off-peak times. | |
Real-Time Price (RTP) | The real-time electricity prices are the APX electricity market prices plus any additional surcharges that are generally applicable to electricity end-users. | |
Price Sensitivity | Price Sensitivity, calculated as: electricity usage/electricity price. | |
Relative Electricity Usage | Relative Daily Electricity Usage. |
Appendix B
Price Sensitivity | ||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) | (13) | (14) | (15) | (16) | (17) | (18) | (19) | (20) | (21) | (22) | (23) | (24) | |
Persons | 0.33 *** | 0.19 *** | 0.16 *** | 0.14 *** | 0.16 *** | 0.28 *** | 0.56 *** | 0.27 *** | 0.16 ** | 0.21 *** | 0.39 *** | 0.57 *** | 0.63 *** | 0.79 *** | 0.70 *** | 0.69 *** | 0.76 *** | 0.81 *** | 0.81 *** | 0.82 *** | 0.71 *** | 0.66 *** | 0.61 *** | 0.39 *** |
(0.05) | (0.04) | (0.04) | (0.03) | (0.03) | (0.04) | (0.05) | (0.05) | (0.07) | (0.08) | (0.09) | (0.10) | (0.10) | (0.09) | (0.08) | (0.07) | (0.07) | (0.06) | (0.07) | (0.06) | (0.06) | (0.06) | (0.06) | (0.05) | |
B. Age | −0.15 *** | −0.10 ** | −0.09** | −0.15 *** | −0.15 *** | −0.07 * | 0.14 *** | −0.12 ** | −0.48 *** | −0.59 *** | −0.76 *** | −0.92 *** | −0.94 *** | −0.86 *** | −0.69 *** | −0.54 *** | −0.27 *** | −0.05 | 0.10 | 0.18 *** | 0.15** | 0.08 | 0.01 | −0.03 |
(0.05) | (0.04) | (0.04) | (0.03) | (0.03) | (0.04) | (0.05) | (0.05) | (0.07) | (0.08) | (0.09) | (0.10) | (0.10) | (0.09) | (0.08) | (0.07) | (0.07) | (0.07) | (0.07) | (0.06) | (0.06) | (0.06) | (0.06) | (0.05) | |
B. Size | 0.01 *** | 0.01 *** | 0.002 *** | 0.003 *** | 0.003 *** | 0.003 *** | 0.002 *** | 0.001 | 0.001 | −0.001 | −0.001 | −0.003 * | −0.003 * | 0.0001 | 0.002 | 0.003 ** | 0.004 *** | 0.01 *** | 0.01 *** | 0.01 *** | 0.01 *** | 0.01 *** | 0.01 *** | 0.01 *** |
(0.001) | (0.001) | (0.001) | (0.001) | (0.0005) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.002) | (0.002) | (0.002) | (0.002) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | |
B. Type | −0.06 | −0.06 | −0.14 *** | −0.20 *** | −0.16 *** | −0.15 *** | −0.04 | −0.005 | 0.02 | 0.01 | 0.04 | −0.05 | −0.02 | −0.19** | −0.25 *** | −0.31 *** | −0.07 | −0.03 | −0.07 | −0.08 | −0.09 | −0.02 | 0.09 | −0.002 |
(0.05) | (0.04) | (0.04) | (0.03) | (0.03) | (0.04) | (0.05) | (0.05) | (0.07) | (0.08) | (0.09) | (0.10) | (0.10) | (0.09) | (0.08) | (0.07) | (0.07) | (0.07) | (0.07) | (0.07) | (0.06) | (0.06) | (0.06) | (0.05) | |
Roof Insul. | −0.18 | −0.13 | 0.01 | 0.16 | 0.19** | 0.24 * | −0.09 | −0.38 ** | −0.53 ** | −0.79 *** | −1.23 *** | −1.20 *** | −1.47 *** | −0.99 *** | −0.95 *** | −0.38 * | 0.16 | 0.40 * | 0.42 * | 0.42 ** | 0.55 *** | 0.27 | −0.31 * | −0.25 |
(0.16) | (0.14) | (0.12) | (0.10) | (0.09) | (0.13) | (0.17) | (0.17) | (0.23) | (0.26) | (0.30) | (0.32) | (0.32) | (0.30) | (0.25) | (0.22) | (0.22) | (0.21) | (0.22) | (0.21) | (0.20) | (0.20) | (0.19) | (0.16) | |
SolarInflux | −0.01 ** | −0.01 *** | −0.02 *** | −0.02 *** | −0.02 *** | −0.02 *** | −0.02 *** | −0.02 *** | −0.02 *** | −0.02 *** | −0.05 ** | 0.36 | ||||||||||||
(0.003) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | (0.003) | (0.005) | (0.02) | (0.52) | |||||||||||||
Constant | 0.44 | 0.53 | 1.51 *** | 1.39 *** | 0.75** | 0.68 | −0.001 | 3.10 *** | 5.38 *** | 7.39 *** | 7.51 *** | 7.89 *** | 8.65 *** | 6.49 *** | 6.06 *** | 3.52 *** | 2.08** | 0.32 | −0.15 | −0.72 | −0.02 | 0.51 | −0.12 | 0.18 |
(0.51) | (0.46) | (0.39) | (0.34) | (0.31) | (0.42) | (0.58) | (0.60) | (0.81) | (0.94) | (1.04) | (1.09) | (1.13) | (1.08) | (0.92) | (0.79) | (0.81) | (0.84) | (0.73) | (0.69) | (0.65) | (0.65) | (0.60) | (0.56) | |
Observations | 1990 | 1923 | 1987 | 1991 | 1991 | 1991 | 1990 | 1991 | 1991 | 1991 | 1991 | 1991 | 1991 | 1991 | 1991 | 1991 | 1991 | 1991 | 1991 | 1991 | 1991 | 1991 | 1999 | 2100 |
R2 | 0.12 | 0.07 | 0.02 | 0.05 | 0.06 | 0.04 | 0.07 | 0.03 | 0.05 | 0.07 | 0.08 | 0.09 | 0.09 | 0.10 | 0.10 | 0.09 | 0.08 | 0.10 | 0.10 | 0.13 | 0.11 | 0.10 | 0.15 | 0.13 |
Adjusted R2 | 0.12 | 0.07 | 0.02 | 0.05 | 0.06 | 0.04 | 0.07 | 0.03 | 0.05 | 0.06 | 0.08 | 0.09 | 0.09 | 0.10 | 0.10 | 0.09 | 0.08 | 0.10 | 0.10 | 0.13 | 0.11 | 0.10 | 0.15 | 0.13 |
F Statistic | 44.94 *** (df = 6; 1983) | 23.63 *** (df = 6; 1916) | 7.86 *** (df = 6; 1980) | 17.64 *** (df = 6; 1984) | 21.00 *** (df = 6; 1984) | 15.22 *** (df = 6; 1984) | 25.33 *** (df = 6; 1983) | 7.96 *** (df = 7; 1983) | 14.13 *** (df = 7; 1983) | 19.76 *** (df = 7; 1983) | 25.28 *** (df = 7; 1983) | 27.88 *** (df = 7; 1983) | 28.67 *** (df = 7; 1983) | 29.98 *** (df = 7; 1983) | 31.26 *** (df = 7; 1983) | 29.59 *** (df = 7; 1983) | 25.23 *** (df = 7; 1983) | 32.48 *** (df = 7; 1983) | 30.67 *** (df = 7; 1983) | 49.79 *** (df = 6; 1984) | 42.30 *** (df = 6; 1984) | 35.68 *** (df = 6; 1984) | 60.02 *** (df = 6; 1992) | 51.44 *** (df = 6; 2093) |
Appendix C
Price Sensitivity | ||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) | (13) | (14) | (15) | (16) | (17) | (18) | (19) | (20) | (21) | (22) | (23) | (24) | |
Persons | −0.15 | −0.08 | −0.14 | −0.19* | −0.15 | −0.10 | 0.14 ** | 0.13 | 0.21 ** | 0.23 * | 0.41 *** | 0.37 *** | 0.39 *** | 0.26 ** | 0.20 | 0.41 *** | 0.54 *** | 0.52 *** | 0.54 *** | 0.59 *** | 0.27 ** | 0.15 | −0.09 | −0.19 |
(0.11) | (0.09) | (0.10) | (0.11) | (0.10) | (0.07) | (0.07) | (0.08) | (0.10) | (0.12) | (0.11) | (0.11) | (0.11) | (0.12) | (0.12) | (0.11) | (0.12) | (0.11) | (0.15) | (0.15) | (0.11) | (0.09) | (0.12) | (0.13) | |
B. Age | 0.13 | 0.01 | 0.01 | 0.06 | 0.06 | 0.05 | −0.09 | −0.08 | −0.10 | −0.06 | −0.17 | −0.10 | −0.11 | 0.04 | 0.02 | −0.02 | −0.22* | −0.36 *** | −0.15 | −0.06 | −0.15 | −0.22 ** | −0.03 | 0.14 |
(0.10) | (0.08) | (0.09) | (0.10) | (0.09) | (0.07) | (0.06) | (0.08) | (0.10) | (0.11) | (0.11) | (0.10) | (0.10) | (0.11) | (0.11) | (0.10) | (0.11) | (0.10) | (0.14) | (0.14) | (0.11) | (0.09) | (0.11) | (0.12) | |
B. Size | 0.02 *** | 0.02 *** | 0.02 *** | 0.02 *** | 0.02 *** | 0.02 *** | 0.02 *** | 0.02 *** | 0.02 *** | 0.03 *** | 0.02 *** | 0.02 *** | 0.02 *** | 0.02 *** | 0.02 *** | 0.02 *** | 0.02 *** | 0.02 *** | 0.01 *** | 0.02 *** | 0.02 *** | 0.02 *** | 0.02 *** | 0.02 *** |
(0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.002) | (0.002) | (0.001) | (0.001) | (0.001) | (0.001) | |
B. Type | 0.18 ** | 0.07 | 0.08 | 0.11 | 0.16 ** | 0.12 ** | 0.14 ** | 0.23 *** | 0.16* | 0.17* | 0.19** | 0.15* | 0.19** | 0.27 *** | 0.24** | 0.23 *** | 0.17* | 0.28 *** | 0.44 *** | 0.38 *** | 0.33 *** | 0.18 ** | 0.22 ** | 0.22 ** |
(0.09) | (0.07) | (0.08) | (0.09) | (0.08) | (0.06) | (0.05) | (0.07) | (0.09) | (0.09) | (0.09) | (0.09) | (0.09) | (0.10) | (0.10) | (0.09) | (0.10) | (0.09) | (0.12) | (0.12) | (0.09) | (0.08) | (0.10) | (0.10) | |
Roof Insul. | −0.16 | −0.18 | −0.16 | −0.14 | −0.35 | −0.60 *** | −0.45 ** | −0.75 *** | −0.98 *** | −1.02 *** | −1.09 *** | −0.96 *** | −0.99 *** | −1.22 *** | −1.15 *** | −1.32 *** | −0.29 | 0.28 | 0.12 | 0.22 | 0.06 | 0.19 | −0.08 | −0.12 |
(0.29) | (0.24) | (0.26) | (0.30) | (0.26) | (0.19) | (0.18) | (0.23) | (0.28) | (0.32) | (0.31) | (0.29) | (0.30) | (0.33) | (0.33) | (0.30) | (0.32) | (0.30) | (0.40) | (0.39) | (0.31) | (0.25) | (0.32) | (0.34) | |
SolarInflux | −0.001 | −0.001 | −0.001 | −0.002 | −0.002 | −0.001 | −0.002 | 0.0000 | −0.003 | −0.01 | −0.03 | 0.09 | ||||||||||||
(0.004) | (0.002) | (0.002) | (0.002) | (0.001) | (0.002) | (0.002) | (0.003) | (0.004) | (0.01) | (0.02) | (0.34) | |||||||||||||
Constant | −1.16 | −1.33 * | −1.71 ** | −2.08 ** | −1.40 ** | −0.96 * | −0.70 | −0.46 | 1.04 | 0.58 | 0.74 | 1.11 | 1.15 | 1.93 | 1.91 | 1.01 | 0.13 | −1.06 | −2.82 *** | −2.55 ** | 0.66 | 0.31 | −0.43 | −0.85 |
(0.87) | (0.76) | (0.79) | (0.90) | (0.71) | (0.55) | (0.64) | (0.75) | (0.87) | (0.90) | (0.93) | (0.86) | (0.95) | (1.19) | (1.24) | (1.09) | (1.00) | (1.08) | (1.04) | (1.10) | (0.97) | (0.84) | (1.06) | (1.03) | |
Observations | 1020 | 986 | 1020 | 1020 | 1020 | 1020 | 1020 | 1020 | 1020 | 1020 | 1020 | 1020 | 1020 | 1020 | 1020 | 1020 | 1020 | 1020 | 1020 | 1020 | 1020 | 1020 | 1020 | 1020 |
R2 | 0.18 | 0.26 | 0.24 | 0.19 | 0.20 | 0.37 | 0.46 | 0.41 | 0.32 | 0.31 | 0.31 | 0.30 | 0.33 | 0.28 | 0.26 | 0.27 | 0.25 | 0.22 | 0.14 | 0.16 | 0.20 | 0.22 | 0.16 | 0.14 |
Adjusted R2 | 0.18 | 0.25 | 0.24 | 0.19 | 0.20 | 0.37 | 0.46 | 0.41 | 0.32 | 0.31 | 0.31 | 0.30 | 0.33 | 0.28 | 0.26 | 0.26 | 0.25 | 0.22 | 0.13 | 0.16 | 0.19 | 0.21 | 0.16 | 0.14 |
F Statistic | 38.18 *** (df = 6; 1013) | 56.04 *** (df = 6; 979) | 53.64 *** (df = 6; 1013) | 39.62 *** (df = 6; 1013) | 42.25 *** (df = 6; 1013) | 99.79 *** (df = 6; 1013) | 143.90 *** (df = 6; 1013) | 99.79 *** (df = 7; 1012) | 69.06 *** (df = 7; 1012) | 64.72 *** (df = 7; 1012) | 65.55 *** (df = 7; 1012) | 61.76 *** (df = 7; 1012) | 70.83 *** (df = 7; 1012) | 55.69 *** (df = 7; 1012) | 51.40 *** (df = 7; 1012) | 52.34 *** (df = 7; 1012) | 49.13 *** (df = 7; 1012) | 41.90 *** (df = 7; 1012) | 22.66 *** (df = 7; 1012) | 31.83 *** (df = 6; 1013) | 41.12 *** (df = 6; 1013) | 46.45 *** (df = 6; 1013) | 32.78 *** (df = 6; 1013) | 28.13 *** (df = 6; 1013) |
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Author | Number of Household Occupants | Building Size | Building Age | Building Type | Other Attributes | |
---|---|---|---|---|---|---|
Schleich and Klobasa (2013) | + | + | No. Appliances | |||
Yohannis et al. (2008) | + | + | + | + | No. Bedrooms | |
McLoughlin et al. (2012) | + | + | + | + | Composition | |
Kavousian et al. (2013) | + | + | insignificant | insignificant | ||
Filippini (2011) | insignificant | + | Income | |||
Alberini et al. (2011) | insignificant | + |
Attributes | Mean | Median | Min | Max | ||||
---|---|---|---|---|---|---|---|---|
Solar Influx | 37.74 | 1 | 0 | 259 | ||||
RTP | TOU | |||||||
Mean | Median | Min | Max | Mean | Median | Min | Max | |
Persons | 2.63 | 2 | 1 | 6 | 2.967 | 3 | 1 | 8 |
Building Age | 2.739 | 3 | 1 | 4 | 2.239 | 2 | 1 | 4 |
Building Size | 158.2 | 140 | 58 | 550 | 153.5 | 123 | 50 | 600 |
Building Type | 3.416 | 3 | 1 | 5 | 3.02 | 3 | 1 | 5 |
Heating Type | 1.185 | 2 | 1 | 2 | 1.219 | 2 | 1 | 2 |
Roof Insulation | 1.853 | 2 | 1 | 2 | 1.76 | 2 | 1 | 2 |
Relative Electricity Usage | ||
---|---|---|
Treatment | RTP | TOU |
0.78 *** | ||
(0.28) | ||
0.95 *** | ||
(0.04) | ||
Solar Influx | −0.0002 *** | −0.0000 *** |
(0.0000) | (0.0000) | |
Constant | −0.10 ** | −0.13 *** |
(0.05) | (0.01) | |
Observations N R2 | 47,275 73 0.34 | 103,515 150 0.48 |
Adjusted R2 | 0.34 | 0.48 |
Price Sensitivity | |
---|---|
Number of Household Occupants | 0.18 *** |
(0.02) | |
Building Age | 0.06 *** |
(0.02) | |
Building Size | 0.02 *** |
(0.0003) | |
Building Type | 0.20 *** |
(0.02) | |
Roof Insulation | −0.47 |
(0.06) | |
RTP * Number of Household Occupants | 0.33 *** |
(0.03) | |
RTP * Building Age | −0.22 *** |
(0.03) | |
RTP * Building Size | −0.02 *** |
(0.0004) | |
RTP * Building Type | −0.25 *** |
(0.02) | |
RTP * Roof Insulation | 0.29 |
(0.08) | |
Solar Influx | −0.01 *** |
(0.0002) | |
Constant | 2.08 *** |
(0.25) | |
Observations | 72,273 |
N R2 | 108 0.13 |
Adjusted R2 | 0.13 |
F Statistic | 803.61 *** (df = 12; 72,259) |
PC1 | PC2 | PC3 | PC4 | PC5 | PC6 | PC7 | PC8 | PC9 | |
---|---|---|---|---|---|---|---|---|---|
Standard deviation | 1.74 | 1.22 | 1.10 | 1.00 | 0.96 | 0.84 | 0.63 | 0.51 | 0.00 |
Proportion of Variance | 0.33 | 0.17 | 0.13 | 0.11 | 0.10 | 0.08 | 0.04 | 0.03 | 0.00 |
Cumulative Proportion | 0.33 | 0.50 | 0.64 | 0.75 | 0.85 | 0.93 | 0.97 | 1.00 | 1.00 |
Building Age | 0.29 | −0.19 | −0.43 | −0.17 | −0.47 | −0.53 | 0.27 | −0.04 | 0.29 |
Building Size | −0.56 | 0.15 | 0.05 | 0.09 | −0.07 | 0.01 | −0.12 | −0.06 | 0.79 |
Building Type | 0.28 | −0.53 | 0.11 | 0.25 | 0.43 | 0.23 | 0.39 | −0.21 | 0.38 |
Solar Panels | −0.03 | −0.24 | 0.37 | −0.80 | 0.25 | −0.24 | −0.18 | −0.09 | 0.10 |
Terrain Type | 0.45 | −0.04 | −0.04 | 0.29 | 0.16 | −0.22 | −0.77 | −0.04 | 0.20 |
Persons | 0.42 | 0.39 | 0.04 | −0.22 | 0.15 | 0.19 | −0.22 | −0.68 | 0.24 |
Solar Heating | −0.04 | −0.05 | −0.73 | −0.35 | 0.17 | 0.49 | −0.21 | −0.14 | 0.05 |
Ventilation Type | 0.27 | 0.15 | 0.09 | −0.06 | 0.08 | −0.01 | 0.12 | −0.67 | 0.14 |
Roof Insulation | 0.25 | −0.16 | 0.33 | −0.11 | −0.67 | 0.54 | −0.18 | −0.10 | 0.11 |
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Koolen, D.; Sadat-Razavi, N.; Ketter, W. Machine Learning for Identifying Demand Patterns of Home Energy Management Systems with Dynamic Electricity Pricing. Appl. Sci. 2017, 7, 1160. https://doi.org/10.3390/app7111160
Koolen D, Sadat-Razavi N, Ketter W. Machine Learning for Identifying Demand Patterns of Home Energy Management Systems with Dynamic Electricity Pricing. Applied Sciences. 2017; 7(11):1160. https://doi.org/10.3390/app7111160
Chicago/Turabian StyleKoolen, Derck, Navid Sadat-Razavi, and Wolfgang Ketter. 2017. "Machine Learning for Identifying Demand Patterns of Home Energy Management Systems with Dynamic Electricity Pricing" Applied Sciences 7, no. 11: 1160. https://doi.org/10.3390/app7111160
APA StyleKoolen, D., Sadat-Razavi, N., & Ketter, W. (2017). Machine Learning for Identifying Demand Patterns of Home Energy Management Systems with Dynamic Electricity Pricing. Applied Sciences, 7(11), 1160. https://doi.org/10.3390/app7111160