Code covered by the BSD License  

Highlights from
Electricity Load Forecasting for the Australian Market Case Study

image thumbnail

Electricity Load Forecasting for the Australian Market Case Study



19 Jun 2011 (Updated )

This is a case study of forecasting short-term electricity loads for the Australian market.

%% Electricity Load Forecasting
% This example demonstrates building and validating a short term
% electricity load forecasting model with MATLAB. The models take into
% account multiple sources of information including temperatures and
% holidays in constructing a day-ahead load forecaster. The models compared
% include Neural Networks and Regression Trees.

%% Import Weather & Load Data
% The data set used is a table of historical hourly loads and temperature
% observations from the New England ISO for the years 2004 to 2008. The
% weather information includes the dry bulb temperature and the dew point.
% This data set is imported from an Access database using the
% auto-generated function _fetchDBLoadData_.

data = fetchDBLoadData('2004-01-01', '2008-12-31');

%% Import list of holidays
% A list of New England holidays that span the historical date range is
% imported from an Excel spreadsheet

[num, text] = xlsread('..\Data\Holidays.xls');
holidays = text(2:end,1);

%% Generate Predictor Matrix
% The function *genPredictors* generates the predictor variables used as
% inputs for the model. For short-term forecasting these include
% * Dry bulb temperature
% * Dew point
% * Hour of day
% * Day of the week
% * A flag indicating if it is a holiday/weekend
% * Previous day's average load
% * Load from the same hour the previous day
% * Load from the same hour and same day from the previous week
% If the goal is medium-term or long-term load forecasting, only the inputs
% hour of day, day of week, time of year and holidays can be used
% deterministically. The weather/load information would need to be
% specified as an average or a distribution

% Select forecast horizon
term = 'short';

[X, dates, labels] = genPredictors(data, term, holidays);

%% Split the dataset to create a Training and Test set
% The dataset is divided into two sets, a _training_ set which includes 
% data from 2004 to 2007 and a _test_ set with data from 2008. The training
% set is used for building the model (estimating its parameters). The test
% set is used only for forecasting to test the performance of the model on 
% out-of-sample data. 

% Create training set
trainInd = data.NumDate < datenum('2008-01-01');
trainX = X(trainInd,:);
trainY = data.SYSLoad(trainInd);

% Create test set and save for later
testInd = data.NumDate >= datenum('2008-01-01');
testX = X(testInd,:);
testY = data.SYSLoad(testInd);
testDates = dates(testInd);

save Data\testSet testDates testX testY
clear X data trainInd testInd term holidays dates ans num text

%% Build the Load Forecasting Model
% Each of the following scripts trains and evaluates a different load
% forecasting model on the training data defined above. Trained models are
% saved to the Models directory

% TBScript

%% Save Trained Model
% We can compact the model (to remove any stored training data) and save
% for later reuse

model = compact(model);
save Models\TreeModel model

%% Test Results
% Load in the model and test data and run the treeBagger forecaster and
% compare to actual load.

load Models\TreeModel
load Data\testSet

%% Compute Prediction
% Predict the load for 2008 using the model trained on load data from 2007
% and before.
forecastLoad = predict(model, testX);

%% Compare Forecasted Load and Actual Load
% Create a plot to compare the actual load and the predicted load as well
% as the forecast error.

ax1 = subplot(2,1,1);
plot(testDates, [testY forecastLoad]);
ylabel('Load'); legend({'Actual', 'Forecast'}); legend('boxoff')
ax2 = subplot(2,1,2);
plot(testDates, testY-forecastLoad);
xlabel('Date'); ylabel('Error (MWh)');
linkaxes([ax1 ax2], 'x');
dynamicDateTicks([ax1 ax2], 'linked')

%% Compute Model Forecast Metrics
% In addition to the visualization we can quantify the performance of the
% forecaster using metrics such as mean average error (MAE), mean average
% percent error (MAPE) and daily peak forecast error.

err = testY-forecastLoad;
errpct = abs(err)./testY*100;

fL = reshape(forecastLoad, 24, length(forecastLoad)/24)';
tY = reshape(testY, 24, length(testY)/24)';
peakerrpct = abs(max(tY,[],2) - max(fL,[],2))./max(tY,[],2) * 100;

fprintf('Mean Average Percent Error (MAPE): %0.2f%% \nMean Average Error (MAE): %0.2f MWh\nDaily Peak MAPE: %0.2f%%\n',...
    mean(errpct(~isinf(errpct))), mean(abs(err)), mean(peakerrpct))

Contact us