Energy

Short Time Electricity Prices and Demand Forecasting

Problem

  • Electricity is a very special commodity, practically non-storable. The power system stability requires a constant balance between production and consumption which means that the demand must be satisfied continuously.
  • The process of deregulation and the introduction of competitive markets have reshaped the traditionally monopolistic and government-controlled power sectors. Nowadays, in many countries all over the world, the production and sale of electricity is traded under competitive rules in free markets.
  • If producers and consumers are able to make reliable predictions of electricity price, they can develop their bidding strategies and their own production or consumption schedules in such a way to reduce the risks or maximize the profits.
  • The costs of over/under contracting and then selling/buying power in the real-time balancing market are typically so high that they can lead to huge financial losses or even bankruptcy.
  • Electric utilities are the most vulnerable, since they generally cannot pass their costs on to the retail consumers. Consequently, prediction of electricity demand and price are significant problems in this sector.

Solution

PRODUCTION
DISTRIBUTION
ELECTRICITY MARKET

Price forecasting

Real price

  • At Eris Innovation we have tried out several methods for short time electricity prices and demand forecasting, from hard computing techniques including time series models and dynamic regression to the soft computing approaches, sometimes referred to as the artificial intelligent tools such as fuzzy neural networks (FNNs) and in general hybrid intelligent systems (HIS).
  • Our models have been tested on the electricity market of Iberian Peninsula, mainly Spanish market, which is commonly used as the test case in several electricity price forecasting studies
  • The obtained results have shown great statistical characteristics as well as high accuracy for a day-ahead hourly price forecasting.

Power Usage Optimization

Datacenter Power Usage Energy Optimization

Problem

  • With the growing popularity of new Cloud applications and services, IT networks and data center's computational demand have experienced a rapid increase, playing the central role in business opportunities and digital services.
  • Data centers represent a critical pillar in businesses for companies in a wide range of industries, raising more and more everyday their economic impact in business operations.
  • Energy consumption of data center facilities has reached the 2% of the global outcome and this energy is mainly produced by non-renewable sources, having not only a high repercussion in the economy but also in the carbon footprint and the thermal impact.
  • The main sources of energy consumption in data centers are due to both computational and cooling contributions.
    • Computational resources, also known as Information Technology (IT), represent around the 60% of the total consumption, where the static power dissipation of idle servers is the dominant contribution.
    • Cooling infrastructure originates around the 30% of the overall consumption. The key factor affecting cooling requirements is the maximum temperature reached on the facility due to the activity of servers.

Solution

It equipment

Cooling

Air movement

Electricity transformer

Lighting

We propose actuations to reduce energy consumption

We monitor data center infrastructure

We use the models to jointly optimize cooling and computational costs of the data center

We derive accurate and flexible models to predict power and energy consumption

  • We monitor data center infrastructure
  • We derive accurate and flexible models to predict power and energy consumption
  • We use the models to jointly optimize cooling and computational costs of the data center
  • We propose actuations to reduce energy consumption