Technology forecastingTechnology forecasting attempts to predict the future characteristics of useful technological machines, procedures or techniques. Researchers create technology forecasts based on past experience and current technological developments. Like other forecasts, technology forecasting can be helpful for both public and private organizations to make smart decisions. By analyzing future opportunities and threats, the forecaster can improve decisions in order to achieve maximum benefits.
CorrelationIn statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the so-called demand curve.
Load-following power plantA load-following power plant, regarded as producing mid-merit or mid-priced electricity, is a power plant that adjusts its power output as demand for electricity fluctuates throughout the day. Load-following plants are typically in between base load and peaking power plants in efficiency, speed of start-up and shut-down, construction cost, cost of electricity and capacity factor. Base load power plants are dispatchable plants that tend to operate at maximum output.
K-means clusteringk-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. k-means clustering minimizes within-cluster variances (squared Euclidean distances), but not regular Euclidean distances, which would be the more difficult Weber problem: the mean optimizes squared errors, whereas only the geometric median minimizes Euclidean distances.
Machine learningMachine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.
AutocorrelationAutocorrelation, sometimes known as serial correlation in the discrete time case, is the correlation of a signal with a delayed copy of itself as a function of delay. Informally, it is the similarity between observations of a random variable as a function of the time lag between them. The analysis of autocorrelation is a mathematical tool for finding repeating patterns, such as the presence of a periodic signal obscured by noise, or identifying the missing fundamental frequency in a signal implied by its harmonic frequencies.
Continual improvement processA continual improvement process, also often called a continuous improvement process (abbreviated as CIP or CI), is an ongoing effort to improve products, services, or processes. These efforts can seek "incremental" improvement over time or "breakthrough" improvement all at once. Delivery (customer valued) processes are constantly evaluated and improved in the light of their efficiency, effectiveness and flexibility.
Emergency managementEmergency management or disaster management is the managerial function charged with creating the framework within which communities reduce vulnerability to hazards and cope with disasters. Emergency management, despite its name, does not actually focus on the management of emergencies, which can be understood as minor events with limited impacts and are managed through the day-to-day functions of a community. Instead, emergency management focuses on the management of disasters, which are events that produce more impacts than a community can handle on its own.
Global Forecast SystemThe Global Forecast System (GFS) is a global numerical weather prediction system containing a global computer model and variational analysis run by the United States' National Weather Service (NWS). The mathematical model is run four times a day, and produces forecasts for up to 16 days in advance, but with decreased spatial resolution after 10 days. The forecast skill generally decreases with time (as with any numerical weather prediction model) and for longer term forecasts, only the larger scales retain significant accuracy.
Customer experienceCustomer experience, sometimes abbreviated to CX, is the totality of cognitive, affective, sensory, and behavioral consumer responses during all stages of the consumption process including pre-purchase, consumption, and post-purchase stages. Nihat Tavşan and Can Erdem bring an extensive elucidation to the customer experience, encompassing the dimensions of consciousness, subjectivity, and interactional nature and define customer experience as the sum of subjective ideas regarding a product or service that occur at a conscious or subconscious level due to direct or indirect interaction of a customer with brand-related stimuli.
DemandIn economics, demand is the quantity of a good that consumers are willing and able to purchase at various prices during a given time. The relationship between price and quantity demand is also called the demand curve. Demand for a specific item is a function of an item's perceived necessity, price, perceived quality, convenience, available alternatives, purchasers' disposable income and tastes, and many other options. Innumerable factors and circumstances affect a consumer's willingness or to buy a good.
Demand curveIn a demand schedule, a demand curve is a graph depicting the relationship between the price of a certain commodity (the y-axis) and the quantity of that commodity that is demanded at that price (the x-axis). Demand curves can be used either for the price-quantity relationship for an individual consumer (an individual demand curve), or for all consumers in a particular market (a market demand curve). It is generally assumed that demand curves slope down, as shown in the adjacent image.
