Building Electricity Demand Forecasting

SHUBHAM SAINI, PANDARASAMY ARJUNAN, AMARJEET SINGHAs part of the work done at

Mobile and Ubiquitous Computing Group

OVERVIEW

The IIIT – Delhi campus has more than 200 smart meters installed, collecting around 10 electrical parameters every 30 seconds.

Important to calculate an accurate baseline, and monitor any deviations from it.

A forecasting pipeline is proposed for predicting the power consumption of an electric load at any given point of time.

Motivation

Energy Consumption Increasing Worldwide India – Energy Forecasting has important role in

formulation of effective energy policies Electricity consumption analysis useful for

monitoring environmental issues

FORECASTING MODELS

Auto-Regressive Integrated Moving Average (ARIMA)

Artificial Neural Networks (ANN) Hybrid ARIMA+ANN EnerNOC

ARIMA (p,d,q)(P,D,Q)

(p,P) - number of lagged variables (d,D) - difference necessary to make the time

series stationary (q,Q) moving average over the number of last

observations.

Where yt and Et are actual value and random error at time t

Artificial Neural Networks

Popular for flexible non-linear modeling Single hidden layer feed-forward network

Where wj and wi,j are model model parameters called connection weights, p is the number of input nodes and q is the number of hidden nodes.

Hybrid ARIMA+ANN

Power consumption composed of linear and non-linear structure

Yt = Lt + Nt

ARIMA able to model linear component Lt

Residuals modeled by ANN

et = Yt - YFt

Final fitted value:

YFt = LFt + NFt

EnerNOC

Based on averaging the load on X days for each interval

D-3 12-1am 1-2am 2-3am 3-4am 4-5am

D-2 12-1am 1-2am 2-3am 3-4am 4-5am

D-1 12-1am 1-2am 2-3am 3-4am 4-5am

Event Day

12-1am 1-2am 2-3am 3-4am 4-5am

Prediction Pipeline

Multiple models can be learned by using different sub-models at each of these stages.

Initial Parameters - Granularity

Very high resolution data available, sampled every 30 seconds

Too small and too large time intervals detrimental to a model's performance

Experimented with 1Hour, 30Minutes, 15 Minutes

Initial Parameters – Forecast Horizon

Forecasting Horizon implies the number of data points a model forecasts into the future.

Days maybe be divided into working/non-working hours, day/night hours, peak/off-peak hours.

SELECTION OF SIMILAR (Y) DAYS

CRITERIA: Previous Business Days Previous Same Days

Lookback Window 4,7,10

7 similar days

14 similar days

Sub-sampling (X) Days

Criteria High X Days

Makes sense for demand-response

Excluding Highest and Lowest Days anomalies could be either due to load failure, holiday,

unpredicted occupancy etc

X:Y = 8:10

X:Y = 6:10

Adjustments – ARIMA+ANN

Training data used to forecast future values includes an additional 2-4 hours of data from the event day.

For example, in order to forecast consumption on the event day for 12PM - 5PM, we use 10AM - 5PM data on the X similar days, as well as 10AM - 12PM data on the event day.

This additional data more accurately reflect load conditions on the event day.

Adjustments - EnerNOC

To adjust the forecasted value of a time interval, for example 12PM - 1 PM, adjustments are done at 11AM

Mean of difference between actual values and the forecasted values between 8AM - 11AM is added(subtracted) to(from) the 12PM - 1PM forecasted value.

Event Day data not always available !!

Results

Brute-force approach to find optimal parameters Over 700 different combinations of parameters tested Varying Parameters:1. No. of similar days - 4, 7, 102. Similarity Criteria - Previous Business Days, Previous Same

Days3. Sub-sampling: High X of Y4. X:Y Ratio - 6:10, 8:105. Models - Hybrid ARIMA+ANN, EnerNOC, Adjusted EnerNOC6. Time Duration - 12AM - 12AM, 12AM - 7AM, 7AM - 12PM,

12PM - 5PM7. Dates - 13-March-2014, 11-March-2014, 5-March-2014, 3-

March-2014, 28-February-2014

Results (Contd.)

Load #1: Academic Building - Floor Total - First Floor

Sample Result

Load #1: Academic Building - Floor Total - First Floor

Number of Similar Days (Y) – 7 X : Y Ratio – 0.8 Similarity Criteria - Previous Same Days Time Duration - 12AM – 7AM Model - Adjusted EnerNOC

Implementation

Developed using the R language for statistical computing version 3.0(RStudio IDE)

Reasons for choosing R over other statistical computing languages like Matlab are:

1. Free and Open-Source2. Graphics and Data Visualization3. Flexible statistical analysis toolkit4. Powerful, cutting-edge analytics5. Robust, vibrant community

UI Design and Layout

GUI for simple data visualization using Shiny web framework v0.98

Tab layout with a sidebar Sidebar contains options to set the forecasting

parameters Main window - training data, and output of

various forecasting models

Time-Series clustering (In Progress)

Global features extracted from the time series through statistical operations

trend seasonality periodicity serial correlation skew, kurtosis chaos nonlinearity self-similarity

Time-Series clustering (In Progress)

Clustering – K-Means or Heirarchical Using global characteristics, group all available

streams into optimal number of clusters For each cluster, find optimal forecasting model

(through the prediction pipeline) For any new stream – assign the stream to one of

the clusters and apply the optimal forecasting model

Questions ???