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.