CSE3CI: Computational Intelligence for Data Analytics - Forecasting Electricity Prices - IT Assignment Help

Assignment Task:

Instructions 

• This is a GROUP assignment. You are permitted to work in groups of up to three. All group members will receive the same mark. You may complete the assignment as an individual, but if you do so, you will be marked in the same way as for a group. 

Problem Description – Forecasting Electricity Prices 

The problem is to forecast electricity price based on historical data. Let the temperature and total demand of electricity at time instant t be T(t) and D(t) respectively. The goal is to predict the recommended retail price (RRP) price by using some historical data as system inputs. The historical data set consists of the following variables: T(t-2), T(t-1), T(t), D(t-2), D(t-1), D(t). The output should be a prediction of the Recommended Retail Price (RRP) of electricity at the next time instant t+1, denoted by P(t+1). 

You have been provided with real-world electricity pricing data from Queensland, Australia. There are two datasets: a training set, to be used for model development; and a test set, to be used to evaluate the performance of your models. Each dataset has the same structure. Rows correspond to successive time instants, and contain seven values: the predictor variables T(t-2), T(t-1), T(t), D(t-2), D(t-1), D(t), and the target variable P(t+1). The objective is to predict the value of P(t+1) on the basis of one or more of the six predictor variables. 

There are five parts to the assignment, described below, with the approximate assessment weighting. Parts 1, 2 and 3 are based on content that has been covered up to then end of Week 5. Content for Part 4 will be covered in Week 6 and 7. 

Part I – Data Preparation (approx. 5%) 

The performance of many systems can be improved through careful preparation of the data. Visualising the electricity prices will reveal that there are potential outliers1 in the dataset; i.e., observations that lie an abnormal distance from other values in a random sample from a population. 

Tasks: 

• Use an appropriate technique to identify and remove outliers of the output variable from the datasets (for both training and test sets). 

• Provide a plot showing the price data before and after the removal of outliers. 

Part 2 – Linear Regression Models (approx. 8%) 

Linear regression is often a good baseline against which to compare the performance of other models. 

Tasks: 

• Apply linear regression to the prediction of electricity prices. 

• For both the training and test sets, provide the Average Relative Error. 

• For both training and test sets, produce a plot showing, for each data point, how the predicted price compares with the actual price. 

Part 3 – Multilayer Perceptron Models (approx. 27%) 

Multilayer perceptrons can sometimes yield better performance over linear models. 

Tasks: 

• Experiment with the application of MLPs to predicting electricity prices. You should try varying MLPRegressor parameters such as the regularization coefficient, the number of training epochs, 

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