{"id":1714,"date":"2021-08-02T11:52:27","date_gmt":"2021-08-02T02:52:27","guid":{"rendered":"http:\/\/gridsolutions.co.jp\/wp\/?page_id=1714"},"modified":"2023-07-24T13:27:29","modified_gmt":"2023-07-24T04:27:29","slug":"ai","status":"publish","type":"page","link":"https:\/\/gridsolutions.co.jp\/en\/ai\/","title":{"rendered":"AI Solutions"},"content":{"rendered":"\n
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Solutions<\/span>
AI<\/h1>\n<\/p><\/div>\n<\/p><\/div>\n
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\n\tHome<\/a>
\n<\/span>\u00bb<\/span>
\n\tAI Solutions
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In VPP, the resource “demand forecast” and “control schedule” affect the aggregator’s payoff, so it is necessary to improve the accuracy. We are developing these two functions to automatically perform forecasts based on time series using machine learning algorithms and deep learning.<\/p>

For example, our AI-based “demand forecast engine” makes demand forecasts through resource performance data and external weather information APIs.<\/p>

Also, in price forecasting, which is the key to power trading, we have developed a “price forecasting engine” using machine learning algorithms and deep learning, and provide spot market price forecasting for the Japan Electric Power Exchange (JEPX).<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n

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