Electricity supply in Tokyo from 2016 to 2020
Last Update：December,16,2020 Created：December,15,2020
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| Title of the dataset | Electricity supply in Tokyo from 2016 to 2020 |
|---|---|
| Provenance of the dataset | Tokyo Electric Power Company Holdings |
| How were the data collected/created? What was the cost? | Accumulated by the company. |
| Data sharing policy | With anyone. |
| Data sharing policy |
About data analysis and simulation
| Type of data: Check all that apply. Use "Other" to specify other types so that we can include them in further updates. | table |
|---|---|
| Variable labels of dataset (the names of the variables) | time(year, month, hour), nuclear_power, fire_power, hydro_power, geothermal_power, biomass, solar_power, wind_power, renewable, sum |
| Outline of data | The history data of electricity supply in Tokyo area by TEPCO from April 1st, 2016 to November 30th, 2020. The data is collected per hour. The energy supply encompasses firepower, nuclear power and renewable energy like solar power, wind power, geothermal power, biomass and so on. In 2020, the firepower still takes most part of energy supply. Solar power energy and wind power make up the most part of renewable energy. However, they fluctuate largely. The data could be used to analyse energy supply change in Tokyo in different time scales. |
| Simulation process | |
| Expected outcome of the process (obtained knowledge, analysis results, output of tools) | Obtain variation features of total electricity supply. Obtain fluctuation features of renewable energy. (Seasonally) Obtain the explanation about how and why the energy structure changes. |
| Anticipation for analyses/simulations other than the typical ones provided above | 1. For further application to reduce carbon emission, this data could be used to consider with renewable energy sector investment. Combined with the investment in different infrastructures for electricity generation, the usefulness analysis of the construction investment could be obtained. 2. Use machine learning method to predict wind power or solar power electricity generation. |
Other
| Comments | |
|---|---|
| What kind of data/tools do you wish to have? | Investment data in different sectors for electricity generation. Carbon emission data. Machine learning tool for electricity generation prediction. |
| Visualized information | |
| Sample data |
February,13,2022
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