Abstract
D.Phil. (Electrical and Electronic Engineering)
Load forecasting is a necessary and an important task for both the electrical consumer
and electrical supplier. Whilst many studies emphasize the importance of determining
the future demand, few papers address both the forecasting algorithm and
computational resources needed to offer a turnkey solution to address the load
forecasting problem. The major contribution that, this paper identified is a turnkey load
forecasting algorithm. A turnkey forecasting solution is defined by a comprehensive
solution that incorporates both the algorithm and processing elements needed to
execute the algorithm in the most effective and efficient manner. An electrical
consumer, namely the operator of a rapid railway system was faced with a problem of
having to forecast the notified network demand and energy consumption.
The forecast period was expected to be between a very short term window for
maintenance reasons and long term for the requirements warranted by the electrical
supplier. The problem was addressed by firstly reviewing the most common forms of
load forecasting for which there are two types. These are statistically based methods
and methods based upon artificial intelligence. The basic principle of a statistical
approach is to approximate or define a curve that best defines the relationship between
the load and its parameters. Regression and similar day approach methods use the
defined correlation of past values in order to forecast the future behaviour.
In other words the future load forecast is forecasted by observing the behaviour of the
factors that influenced the load behaviour in the past. The underlying factors that
influence the final load may be identified by means of a top down drill down approach.
In this way both the load factors and influential variables may be identified. This paper
makes use of relevance trees to create a structure of load and influential variables. For
a regression forecasting model, the behaviour of the load is modelled according to
weather and non-weather variables. The load may be stochastic or deterministic, linear
or nonlinear. One of the biggest problems with statistical models is the lack of
generality. One model may yield more acceptable results over another model simply
because of the sensitivity of the model to one load element that defines the model
significantly. Regression type forecast models are an example of this where the
elements that define the load are broadly divided into weather and non-weather
elements. It is important that the correlation curve reflects the true correlation between
the load and its elements. The recursive properties of a statistical based techniques
(Kalman filter) allows that the relationship be refined. For methods such as neural
networks, the relationship between the load elements that define the future load
behaviour is learnt by presenting a series of patterns and then a forecast model is
derived.
Rigorous mathematical equations are replaced with an artificial neural network where
the load curve is learnt. Unlike a statistical based approach (ARMA models), the load
does not first need to be defined as a stochastic or deterministic series. In terms of a
stochastic approach (non stationery process), the load first would have to be brought to
a stationery process. For artificial neural networks, such processes are eliminated and
the future forecast is derived faster in terms of a turnkey approach (tested solution).
Artificial Neural Networks (ANN) has gained momentum since the eighties. Specifically
in the area of forecasting, neural networks have become a common application. In this
thesis, data from a railway operator was used to train the neural network and then future
data is forecasted. Two embedded processing elements were then evaluated in terms
of speed, memory and ability to execute complex mathematical functions (libraries).
These were namely a Complex Programmable Logic Device (CPLD) and
microcontroller (MCU). The ANN forecasting algorithm was programmed on both a
MCU and PLD and compared by means of timing models and hardware platform
testing. The most ideal turnkey solution was found to be the ANN algorithm residing on
a PLD. The accuracy and speed results surpassed that of a MCU.