WeChat to check the power consumption of the power grid

How to predict power consumption?

There are also works using SVR, the regression based on SVM and so on, to forecast power consumption in combination with other techniques such as fuzzy-rough feature selection [ 37 ], particle swarm optimisation algorithms [ 36] and chaotic artificial bee colony algorithm [ 35 ].

What is power demand forecasting based on meter measurement?

One resulting benefit is that the accurate, fine-grained power demand forecasting can be carried out based on such meter measurement. Such forecasting based on the historical data facilitates power generation scheduling and power dispatching in a future period.

Is Powernet suitable for a day-ahead electricity forecast?

By doing so, grid operators can evaluate whether PowerNet is suitable for certain tasks that require different lengths of prediction period, such as bidding in the day-ahead electricity market and day-ahead electricity scheduling which require the one day-ahead forecasting results [ 23 ].

What is the IEA real-time electricity map?

The IEA real-time electricity map displays electricity demand, generation and spot prices from more than 50 sources. Data is available historically, as well as daily or hourly, and at country or regional levels. Explore the map to discover visuals and analysis. We are continuously looking for new data sources.

How can Powernet be used for forecasting?

The forecasting results from PowerNet can be interpreted differently depending on the tasks, e.g. the power demand at some time in the future or the expected normal consumption at that time. In the latter sense, PowerNet can be used to define the normal consumption behaviour based on which further anomaly detection can be carried out.

Can Powernet predict apartment energy demand?

The results show that PowerNet can reduce and bound the error over these competitors, even in the prediction of individual apartment energy demand. We further evaluate the forecasting model under different forecasting duration and re-training frequency using publicly available datasets.