Matrica's Energy Load Forecasting and Demand Management System:
Nominator:
Nominator is the Core Logic Module which drives numerous function and feature modules associated with Energy Load Forecasting and Energy Demand Forecasting. The system has been designed to be modular and is therefore highly configurable, benefiting the user by conforming to their specific requirements whilst being capable of expanding as the user's requirements grow. This cost effective approach is supported by our customers.
A brief outline of Nominator's functions and features modules are shown below:
Nominator Functions:
- Forecasting Methodologies :
Nominator uses traditional mathematical algorithms, Multivariate Regression and Neural Networks. These combine with the traditional equivalent day and unique combination method. In addition, profile based forecasting is also available. For this Matrica uses ProCalc, our own profile calculator and generator, and the EU profiles already available within the system. Nominator also provides an advanced level of Confidence Limits or Intervals against each forecast.
Nominator has many built in variables eg: weather and holidays, but also offers the user complete flexibility to add their own eg: GDP and Population Growth.
- Granularity and Forecasting Horizons :
Any granularity can be defined for the system, traditionally being half hourly for Electricity in the UK, quarter hourly for most of the global markets and hourly and daily for Gas. Nominator can forecast for short term day and days ahead to weekly, monthly, quarterly or yearly horizons. For long term forecasting the normal observed periods are 5 to 10 years ahead.
Nominator Features:
- Wind Generation Forecasting :
As the world becomes conscious of using renewable sources of energy, the
proliferation of wind farms and wind generated source of power is proof
of intent. Nominator uses a series of complex algorithms
and weather variables to generate reliable load
forecasts to manage this highly volatile energy source.
Nominator can be run in an automated mode by user defined scripts through all processes of Nominator - importing, modelling, forecasting and exporting.
Scenario modelling or "what-if" is an essential practice undertaken by many users to ascertain the impact of the unknown or unforeseen events. Nominator can perform scenario analysis on any variables and includes Monte Carlo simulation.
Increasingly, forecasts are becoming part of management reporting and the Aggregator presents information in any order of the data stored within the systems hierarchy, this includes both aggregation and breakdown.
ProCalc is designed to permit users to create and compare a variety of user profiles ranging from individual sites to an entire portfolio at customer or even national levels. In countries where central bodies exist profile co-efficients may be available and these can be directly imported into ProCalc for the generation of consumption profiles.
Has been developed to meet the ever increasing requirements of users in multiple markets. The Nominator system permits remote access and use via the Internet and can be deployed across systems in a virtual mode. This versatility has permitted our users to use Nominator for multi-utilities in multi regions, all controlled from one central installation.
The net imbalance volume ("NIV") calculator regressively analyses errors and projects ahead of time to generate the probability of accruing imbalance totals. This is an invaluable utility to prevent ballooning costs and this module runs as a standard module in Nominator.
- Non-Energy Demand Forecasting :
Nominator takes full advantage of its generic
structure and database flexibility to permit using the system for non-energy
specific forecasting and business modelling. A good example is the use
of Nominator for price forecasting and
in the construction of an intelligent Forward Curve Builder used
by Risk Managers and Energy Traders. The Core of Nominator has also been
used in other areas such as transportation and behavioural models used
by the Financial sector.
