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I am interested if someone can review this process and give me some tips. (I don't have any data science coworkers or friends to collaborate with...) When the script (.py file) below runs, it will output/print:

The total savings are 9793 kWh + - 192 kWh

The total savings are 9793 kWh + - 2.0 %

Ultimately I am attempting calculate "precision" with my results (+ - 192 kWh AND + - 2.0 %) ..

For starters, I cant find this info online so here's a snip from a book I am referencing on calculating data set standard error, then absolute precision, relative precision.

For retrieving my critical t value, I am following these steps from machinelearningmastery.com. This is where the scipy.stats import t package is required.

I cant remember where degrees of freedom comes into play. The machinelearningmastery example, the author uses df = 10 So until I can figure out this, I left that as a default value in my code below. If someone wanted to copy and paste the code below it should run...

I cant remember from college where degrees of freedom comes into play and how to utilize it. The length of the data when its converted into df2 Pandas dataframe is 31, which represents one months data 31 days. Any tips greatly appreciated..

import os

import numpy as np

import pandas as pd

import math

from scipy.stats import t



#actual electrical energy in kWh

actual = 40000



#calculated savings in kWh, output from Keras regression

data = [np.array([[1083.8748]], dtype='float32'), np.array([[998.98773]], dtype='float32'), np.array([[1137.0487]], dtype='float32'), np.array([[1077.2798]], dtype='float32'), np.array([[926.41284]], dtype='float32'),

np.array([[1030.7125]], dtype='float32'), np.array([[1028.0048]], dtype='float32'), np.array([[523.9799]], dtype='float32'), np.array([[1125.092]], dtype='float32'), np.array([[1119.7738]], dtype='float32'),

np.array([[918.6966]], dtype='float32'), np.array([[1112.5186]], dtype='float32'), np.array([[555.6942]], dtype='float32'), np.array([[1096.5643]], dtype='float32'), np.array([[826.35657]], dtype='float32'),

np.array([[1014.35406]], dtype='float32'), np.array([[1027.6962]], dtype='float32'), np.array([[924.20087]], dtype='float32'), np.array([[1035.217]], dtype='float32'), np.array([[1008.9658]], dtype='float32'),

np.array([[970.54047]], dtype='float32'), np.array([[847.0671]], dtype='float32'), np.array([[913.5519]], dtype='float32'), np.array([[1047.0747]], dtype='float32'), np.array([[920.0606]], dtype='float32'),

np.array([[994.2266]], dtype='float32'), np.array([[991.4501]], dtype='float32'), np.array([[972.43256]], dtype='float32'), np.array([[934.8802]], dtype='float32'), np.array([[912.04004]], dtype='float32'), np.array([[1131.297]], dtype='float32')]



#convert data to pandas series then DataFrame

df = pd.Series(data)

df2 = pd.DataFrame(df)



#define sum, standard deviation, mean of calculated savings data

total = df2.sum()

totalStd = np.std(df2.values)

totalMean = df2.mean()



#calculate saings

diff = int(actual - total)



#compute precision of results, starting with standard error

stdErr = totalStd / math.sqrt(len(total.index))



# define probability & degrees of freedom

p = 0.90

degf = 10



# retrieve t value <= probability

t = t.ppf(p, degf)



#calculate absolute precision

absPrec = t * stdErr 



#calculate relative precision

relPrec = (100 * (absPrec / diff)).round(decimals=1)





#print results

absSavings = f'The total savings are {int(diff)} kWh + - {int(absPrec)} kWh'

print(absSavings)



relSavings = f'The total savings are {int(diff)} kWh + - {float(relPrec)} %'

print(relSavings)