I have time-series energy consumption data for a duration of one-month. The frequency of data is half-hourly. The features of dataset aretemperature - temperature value at particular time instanthumidity -    humidity value at particular time instanttimeofday -  this corresponds to 48 half-hourly durations of a day.weekday -  day of the week (value between 1 - 7)prevday1 -  energy consumption at the same time-instant on previous dayprevday2 - energy consumption at the same time-instant on a day before the  previous dayprev_instant1 - energy consumption at the previous time instant. For example, for 7 pm, previous_instant1 is 6:30 pmprev_2_hour - average energy consumption during the last two hoursUsing these variable I need to forecast power (energy) consumption. The pairs plot of these variables is I started with a linear model, but it seems that the model is performing very badly. I want to know is there any automatic technique, which can check all possible combinations of predictor variables and output the best fitted model. I can check all models (linear combinations, non-linear, splines etc) manually, but it will take huge time.UPDATEI am forecasting in real-time at a half-hourly rate (very short term forecasting). Also, I use separate models corresponding to different half-hours of the day. For example, In a day we have 48 half-hours so corresponding to each half-hour I have a separate model. For the whole process, I use following approach:Create a training data set with above mentioned features. This training data set is of one month duration (30 days)In the next step, I forecast for the next day (testing day) using specific models for each of 48 half-hour durationsAt the end of the day, I retrain all my 48 models with recently updated data (recent historical 30 days)Steps 2 and 3 continue till stopping condition. Stopping condition used is the no. of testing days. A screenshot of my training data is as:head(training_data)tail(training_data)
Underlying model for prediction using different prediction variables
r;predictive modeling;regression;time series;feature selection