[:         "input": "Based on the historical load data, please predict the load consumption in the next day. The region for prediction is TAS. The start date of historical data was on 2019-3-31 that is Weekend, and it is not a public holiday. The data frequency is 30 minutes per point. Historical data covers 1 day. The date of prediction is on 2019-4-1 that is Weekday, and it is not a public holiday. Weather of the start date: the minimum temperature is 281.03; the maximum temperature is 290.01; the humidity is 66.0; the pressure is 1024.0.  Weather of the prediction date: the minimum temperature is 279.91; the maximum temperature is 291.79; the humidity is 81.0; the pressure is 1026.0. There is no suitable news for long-term effect on future load consumption.In 2019-03-31 19:20:00, the news that had the Short-Term Effect on Today's Load Consumption is that Temperatures plummeted over the weekend with Sydneysiders shivering their way through the coldest March night in 20 years. The region that may be influenced is NSW. The rationality is that Extreme cold weather can significantly increase energy consumption due to additional heating requirements; therefore, this event may lead to an immediate spike in load consumption as people attempt to keep their homes warm.In 2019-03-31 17:22:00, the news that had the Real-Time Direct Effect on Today's Load Consumption is that Thousands of footy fans were left stranded while others were packed in like sardines on trains and buses as AdelaideMetro struggled to cope with 53,000 fans at the Crows grand final. The region that may be influenced is SA. The rationality is that The transportation of such a large number of individuals requires significant energy usage, potentially straining the public transportation system and increasing load consumption in real-time due to the use of trains, buses, and supporting infrastructure.",