ML Methodology for Time Series Problem on Energy Consumption

Hi, I am working on a machine learning problem. My question is regarding the dataset and how best to approach the problem of energy consumption prediction. I am trying to predict building energy consumption with a dataset consisting of 66,958 records. There are 7 variables including...

date_time: Date variable Tdb: Outdoor temperature RH: Relative Humidity SR_global: Solar Radiation (Global) SR_direct: Solar Radiation (Direct) SR_diffuse: Solar Radiation (Diffuse) tot_e_pw(kw): Total energy consumption

Each record is 10 minute intervals and spans between 01/01/2017 00:00 to 31/08/2018 23:50. However, there are some significant gaps in the data, I have typed up the major periods where there is data below. There is no data outside of these date times.

BLOCKS OF DATA:

1st Major Period: 01/01/2017 00:00 - 22/01/2017 23:50

2nd Major Period: 06/02/2017 00:00 - 04/05/2017 23:50

3rd Major Period: 17/07/2017 00:10 - 31/12/2017 23:50

4th Major Period: 26/02/2018 00:00 - 31/08/2018 23:50

I want to know how I should approach this problem. Do I deal with this as a time series prediction problem or do I approach it differently? If I approach this differently then what kind of machine learning models should I use?

The link for the dataset is https://github.com/adamusmani95/Energy-Consumption-Prediction/blob/main/full_data.csv