User consumption prediction analysis

Honestly it is really hard to predict the user consumption by using GPs. Previously, I have tried to run a few examples on the real user usage, unfortunately the result were not good. I think to be able to do that, we need some help from the users, who directly use their home devices.

People (or users) often have a plan of what they are going to do, typically day-ahead or week-ahead. Specifically, with the help of technology, they can do planning in their own calendar (e.g, google calendar), then synchonises all activities to their phone for the notification and better time management. Therefore, I think if we can access to this type of information, or if we can get users to support the prediction by working with the agent, we can predict more accurately. However, I am still not too sure how we can do that.

Back to the user consumption prediction analysis, I try to do some analysis on the labels, which were annotated by the users, to see if we can get something from there. I firstly imported the list of events from the excel file. In this file, it has the annotated labels for all users, then we need to filter the data of the specific user to do some test. After that, I sort the data in ascending order of the starting time of the event. Then, I do some calculation on labels so that each label has a starting time step t, running for a length of s. Each label contains an energy usage as well as a baseline usage, however we only want to focus on energy usage at this time.

Up to this stage, we have a list of labels in which each label has a name, a starting time step t, the length of a running time, and an usage. For example, kettle starts at time 3 to 7, with a consumption of 0.2023 kWh; washing machine runs from time 7 to 11, with a consumption of 0.305 kWh. I wonder how we can apply GPs to predict the future labels, as each label can has 3 parameters (label name, time, and consumption). One solution I can think of is to break down the list of labels to individual single category, for example under the label category such as kettle, washing machine,..., then apply GPs to predict time and usage. Hence, we aggregate all single prediction to get the final graph. I am not sure at this stage and looking for a suggestion.

In addition, Poison process can be used to calculate the probability of the labels, which should appear in the time step t'. But how can we combine the Poison process and GPs to make a good prediction, I still have not figure it out yet.