Energy Electricity Forecasting

Forecast energy demand across regions and building types using panel data with 50 entities tracked over 100 time periods.

Dataset Exploration

The Energy Electricity dataset tracks power consumption metrics across different buildings and time periods. Features include price (log-transformed), energy efficiency programs, household activity levels, and building characteristics.

Panel data structure: 50 entities (buildings/regions) tracked over 100 time periods (5,000 total rows, 11 columns).

Fit Data (first 5 rows)

Time	Individual	log(Price)	Energy Eff.	Household Act.	log(P)×Eff.	Building Size	Building Type	Energy Demand
1	1	-0.84	2	3	-1.68	8.23	1	634.54
2	1	-1.1	2	2	-2.2	8.23	1	454.57
3	1	-0.84	2	2	-1.68	8.23	1	470.83
4	1	-0.81	2	2	-1.62	8.23	1	409.56
5	1	-1	2	2	-2	8.23	1	428.04

Forecast Data (first 3 rows)

The forecast file covers future time periods without the Energy Demand target column.

Time	Individual	log(Price)	Energy Eff.	Household Act.	log(P)×Eff.	Private Cust.	Building Size	Building Type
101	1	-1.34	1	2	-1.34	1	8.09	3
102	1	-0.67	1	1	-0.67	1	8.09	3
103	1	-0.66	1	2	-0.66	1	8.09	3

Download sample datasets from GitHub

Code Walkthrough

Step 1: Initialize

Python

import pandas as pd
from datfid import DATFIDClient

client = DATFIDClient(token="your_DATFID_token")

Step 2: Fit the Model

Python

url_fit = "https://raw.githubusercontent.com/datfid-valeriidashuk/sample-datasets/main/Energy_Electricity.xlsx"
df = pd.read_excel(url_fit)

result = client.fit_model(
    df=df,
    id_col="Individual",
    time_col="Time",
    y="Energy Demand",
    current_features="all",
    filter_by_significance=True
)

Step 3: Forecast

Python

url_forecast = "https://raw.githubusercontent.com/datfid-valeriidashuk/sample-datasets/main/Energy_Electricity_forecast.xlsx"
df_forecast = pd.read_excel(url_forecast)

forecast = client.forecast_model(df_forecast=df_forecast)

Analysis Results (Model Fit)

Formula

Energy Demand ~ α1*Intercept + α2*Building Size + α3*Building Type + α4*Private Customer + β1*log(Price) + β2*Energy Efficiency Programs + β3*Household Activity + β4*Weekend

Alpha Estimates (Time-Invariant)

Variable	Estimate	T-stat	Interpretation
Building Type	+99.80	63.88	Each building type category adds ~100 units of demand
Private Customer	+40.38	13.29	Private customers have ~40 units higher baseline demand
Building Size	+32.25	6.02	Larger buildings consume more energy
Intercept	+35.00	0.83	Baseline demand level

Beta Estimates (Time-Varying)

Variable	Estimate	T-stat	Interpretation
log(Price)	-180.27	33.31	Higher prices significantly reduce energy demand (price elasticity)
Energy Efficiency Programs	-101.00	24.20	Each efficiency program unit reduces demand by ~101 units
Household Activity	+50.44	19.72	Higher household activity increases energy consumption
Weekend	+22.78	7.23	Weekend days have ~23 units higher demand

Model Performance

0.557

R² Overall

0.897

R² Between

0.305

R² Within

83.83

MAE

10,995

MSE

Try it yourself: Run this exact analysis in the Free Playground — select "Energy Electricity" from the sample datasets.