Supervised learningSupervised learning (SL) is a paradigm in machine learning where input objects (for example, a vector of predictor variables) and a desired output value (also known as human-labeled supervisory signal) train a model. The training data is processed, building a function that maps new data on expected output values. An optimal scenario will allow for the algorithm to correctly determine output values for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way (see inductive bias).
Thermal conductivityThe thermal conductivity of a material is a measure of its ability to conduct heat. It is commonly denoted by , , or . Heat transfer occurs at a lower rate in materials of low thermal conductivity than in materials of high thermal conductivity. For instance, metals typically have high thermal conductivity and are very efficient at conducting heat, while the opposite is true for insulating materials like mineral wool or Styrofoam.
Geothermal energyGeothermal energy is thermal energy in the Earth's crust. It combines energy from the formation of the planet and from radioactive decay. Geothermal energy has been exploited as a source of heat and/or electric power for millennia. Geothermal heating, using water from hot springs, for example, has been used for bathing since Paleolithic times and for space heating since Roman times. Geothermal power, (generation of electricity from geothermal energy), has been used since the 20th century.
Home automationHome automation or domotics is building automation for a home. A home automation system will monitor and/or control home attributes such as lighting, climate, entertainment systems, and appliances. It may also include home security such as access control and alarm systems. The phrase smart home refers to home automation devices that have internet access. Home automation, a broader category, includes any device that can be monitored or controlled via wireless radio signals, not just those having internet access.
Learning stylesLearning styles refer to a range of theories that aim to account for differences in individuals' learning. Although there is ample evidence that individuals express personal preferences for how they prefer to receive information, few studies have found any validity in using learning styles in education. Many theories share the proposition that humans can be classified according to their "style" of learning, but differ in how the proposed styles should be defined, categorized and assessed.
Energy industryThe energy industry is the totality of all of the industries involved in the production and sale of energy, including fuel extraction, manufacturing, refining and distribution. Modern society consumes large amounts of fuel, and the energy industry is a crucial part of the infrastructure and maintenance of society in almost all countries.
Seasonal thermal energy storageSeasonal thermal energy storage (STES), also known as inter-seasonal thermal energy storage, is the storage of heat or cold for periods of up to several months. The thermal energy can be collected whenever it is available and be used whenever needed, such as in the opposing season. For example, heat from solar collectors or waste heat from air conditioning equipment can be gathered in hot months for space heating use when needed, including during winter months.
Energy StarEnergy Star (trademarked ENERGY STAR) is a program run by the U.S. Environmental Protection Agency (EPA) and U.S. Department of Energy (DOE) that promotes energy efficiency. The program provides information on the energy consumption of products and devices using different standardized methods. The Energy Star label is found on more than 75 different certified product categories, homes, commercial buildings, and industrial plants. In the United States, the Energy Star label is also shown on the Energy Guide appliance label of qualifying products.
Boosting (machine learning)In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones. Boosting is based on the question posed by Kearns and Valiant (1988, 1989): "Can a set of weak learners create a single strong learner?" A weak learner is defined to be a classifier that is only slightly correlated with the true classification (it can label examples better than random guessing).
Energy marketEnergy market is a type of commodity market that deal with electricity, heat, and fuel products. Major commodities being natural gas and electricity. Other commodities traded in the energy market are: oil, coal, carbon emissions (greenhouse gases), nuclear power, solar energy, and wind energy. Due to the difficulty in storing and transporting energy, current and future prices in energy are rarely linked. This is because energy purchased at current prices is difficult to store and sell at a later date.
