Introduction

Electricity demand or load forecasts inform both industrial and governmental decision making processes, from energy trading and electricity pricing to demand response and infrastructure maintenance. Electric load forecasts allow Distribution System Operators (DSOs) and policy makers to prepare for the short and long term future. For informed decisions to be made, particularly within industries that are highly regulated such as electricity trading, the factors influencing electricity demand need to be understood well. This is only becoming more urgent, as low carbon technologies (LCT) become more prevalent and consumers start to generate electricity for themselves, trade with peers and interact with DSOs.

Extreme Value Statistics

When studying peaks in electricity demand, we may be interested in understanding the risk of a certain large level for demand being exceeded. For example, there is potential interest in finding the probability that the electricity demand of a business or household exceeds the contractual limit. An alternative, yet in principle equivalent way, involves assessment of maximal needs for electricity over a certain period of time, like a day, a week or a season within a year. This would stem from the potential interested in quantifying the largest electricity consumption for a substation, household or business.

Case Study

In the previous chapter, we looked at load measurements for all households together and we ignored their chronological order. In contrast, in this chapter, we are interested in short term forecasting of household profiles individually. Therefore, information about the time at which measurements were taken becomes relevant.

Extreme Value Theory

From travel disruptions to natural disasters, extreme events have long captured the public’s imagination and attention. Due to their rarity and often associated calamity, they make waves in the news (Fig. 3.1) and stir discussion in the public realm: is it a freak event? Events of this sort may be shrouded in mystery for the general public, but a particular branch of probability theory, notably Extreme Value Theory (EVT), offers insight to their inherent scarcity and stark magnitude. EVT is a wonderfully rich and versatile theory which has already been adopted by a wide variety of disciplines in a plentiful way. From its humble beginnings in reliability engineering and hydrology, it has now expanded much further; it can be used to model the occurrences of records (say for example in athletic events) or quantify the probability of floods with magnitude greater than what has been observed in the past, i.e it allows us extrapolate beyond the range of available data!

Short Term Load Forecasting

Electrification of transport and heating, and the integration of low carbon technologies (LCT) is driving the need to know when and how much electricity is being consumed and generated by consumers. It is also important to know what external factors influence individual electricity demand.