PredictionIO electric load forecasting engine

Overview

This is a PredictionIO engine for electric load forecasting.

Usage

To use this engine you should first get yourself familiar with PredicionIO. You can do so easily by going through one of the PredictionIO official quick start guides, like this one: Quick Start - Classification Engine Template.

Data format

The data that we are training our engine on contains values of hourly energy consumption for a group of circuits.

More formally, each of our events contains of 3 components: circuit id (represented by an integer number), date (represented by unix time stamp) and value of energy consumption (represented by floating point number).

You can see example data looking at data.csv. The file follows comma-separated values format. Rows in the first column contains timestamps. Every column, but first, begins with an integer value representing the circuit id. The import_data.py file is specifically created for importing data of such a format to our application. Example usage of the file:

python data/import_eventserver.py --access_key <APP_ACCESS_KEY> --file <DATA_FILE>

Global Energy Forecasting Competition 2012

You can also easily import data of format as in GEFComp2012 Load_history.csv file using import_data_gefcom2012.py. The zone_id is imported to our application as circuit id.

Query format

Query consists of circuit id (represented by an integer number) and time (represented by unix time stamp). Sending example query using PredictionIO Python SDK:

import predictionio
engine_client = predictionio.EngineClient(url="http://localhost:8000")
print engine_client.send_query({"circuit_id":1, "timestamp":1422298800})

Algorithm

The algorithm used in this engine is linear regression with stochastic gradient descent from Spark MLlib.

Evaluation

The engine uses root mean squared error as metric and k-fold splitting technique for evaluation. To evaluate the model on your data, after uploading it, all you need to do is type in:

pio eval detrevid.predictionio.loadforecasting.RMSEEvaluation detrevid.predictionio.loadforecasting.EngineParamsList

Note

For a better performance, you may want to expand JVM memory by adding "-- --driver-memory <MEMERY_AMOUNT>". For example:

pio eval detrevid.predictionio.loadforecasting.RMSEEvaluation detrevid.predictionio.loadforecasting.EngineParamsList -- --driver-memory 5g

You can find algorithms parameters used in evaluation in Evaluation.scala. You may want to learn more about evalutaion before you start using it, you can do so by reading Tuning and Evaluation.