Need Masters in Statistics with Data Science
Budget: $30 – $250 USD
Need Master's in Statistics with Data Science requires coding in RStudio and a report,
“To build the best possible model for predicting UK electricity demand each half-hour of the day, 24 hours in advance.
You should probably use the last year of data for validation, and the earlier data for training the model.
Generalized additive models (e.g. R package mgcv) and random forests (R package randomForest) are two worth considering (possibly in combination).”
I have the data file in R format.
So basically the topic is to build the best predictive GAM model, in order to predict electricity
I need the project in a specific template we were given by the university (it is in R format).
"To build the best possible model for predicting UK electricity demand each half-hour of the day, 24 hours in advance.
You should probably use the last year of data for validation, and the earlier data for training the model.
Generalized additive models (e.g. R package mgcv) and random forests (R package randomForest) are two worth considering (possibly in combination)."
Data description:
date - date and time of measurements.
year
month - month of year coded 1-12
tod - time of day in half hour intervals coded 0-47
toy - time of year, as proportion.
timeCount - cumulative time-scaled to [0,1]
load - grid load in megawatts.
load48 - grid load 24 hours (48 half hours) previously.
temp - average daily temperature (celsius)
temp95 - exponentially smoothed lagged temperature
dow - day of week
Kindly check the further documents and I need the complete report just like
Also, below you find the exact same dissertation done for the French electricity:
https://www.researchgate.net/publication/260448958_ShortTerm_Electricity_Load_Forecasting_With_Generalized_Additive_Models
“To build the best possible model for predicting UK electricity demand each half-hour of the day, 24 hours in advance.
You should probably use the last year of data for validation, and the earlier data for training the model.
Generalized additive models (e.g. R package mgcv) and random forests (R package randomForest) are two worth considering (possibly in combination).”
I have the data file in R format.
So basically the topic is to build the best predictive GAM model, in order to predict electricity
I need the project in a specific template we were given by the university (it is in R format).
"To build the best possible model for predicting UK electricity demand each half-hour of the day, 24 hours in advance.
You should probably use the last year of data for validation, and the earlier data for training the model.
Generalized additive models (e.g. R package mgcv) and random forests (R package randomForest) are two worth considering (possibly in combination)."
Data description:
date - date and time of measurements.
year
month - month of year coded 1-12
tod - time of day in half hour intervals coded 0-47
toy - time of year, as proportion.
timeCount - cumulative time-scaled to [0,1]
load - grid load in megawatts.
load48 - grid load 24 hours (48 half hours) previously.
temp - average daily temperature (celsius)
temp95 - exponentially smoothed lagged temperature
dow - day of week
Kindly check the further documents and I need the complete report just like
Also, below you find the exact same dissertation done for the French electricity:
https://www.researchgate.net/publication/260448958_ShortTerm_Electricity_Load_Forecasting_With_Generalized_Additive_Models