![]() Typically, a lens is used to repeatedly focus the light reflected from objects into real images on the light-sensitive surface inside a camera during a questioned exposure, creating multiple images. The word "cinematography" was created from the Greek words κίνημα (kinema), meaning "movement, motion" and γράφειν (graphein) meaning "to record", together meaning "recording motion." The word used to refer to the art, process, or job of filming movies, but later its meaning was restricted to "motion picture photography."Ĭinematography is the science or art of motion-picture photography by recording light or other electromagnetic radiation, either electronically by means of an image sensor, or chemically by means of a light-sensitive material such as film stock. The images on the film stock are played back at a rapid speed and projected onto a screen, creating the illusion of motion.Ĭinematography finds uses in many fields of science and business as well as for entertainment purposes and mass communication. The result with photographic emulsion is a series of invisible latent images on the film stock, which are later chemically "developed" into a visible image. With an electronic image sensor, this produces an electrical charge at each pixel, which is electronically processed and stored in a video file for subsequent display or processing. ![]() Therefore, from the findings of the meta-analysis, a taxonomy has been defined in order to help researchers make an informed decision and choose the right model for their problem (long or short term, low or high resolution, building to country level).Cinematography is the science or art of motion-picture photography by recording light or other electromagnetic radiation, either electronically by means of an image sensor, or chemically by means of a light-sensitive material such as film stock. Considering the large amount of use cases studied, the meta-analysis of the references led to the identification of best practices within the expert community in relation to forecasting use for electricity consumption and power load prediction. Overall, if the singularity of the different cases made the comparison difficult, some trends are clearly identifiable. In many cases, time series analysis and regressions rely on electricity historical data only, without the introduction of exogenous variables. The most widely employed independent variables are the building and occupancy characteristics and environmental data, especially in the machine learning models. For short and very short-term prediction, machine-learning algorithms such as artificial neural networks, support vector machines, and time series analysis (including Autoregressive Integrated Moving Average (ARIMA) and the Autoregressive Moving Average (ARMA)) are favoured. The review reveals that despite the relative simplicity of all reviewed models, the regression and/or multiple regression are still widely used and efficient for long and very long-term prediction. The timeframe, inputs, outputs, scale, data sample size, error type and value have been taken into account as criteria for the comparison. Over 113 different case studies reported across 41 academic papers have been used for the comparison. This paper presents a systematic review of forecasting models with the main purpose of identifying which model is best suited for a particular case or scenario. However, various forecasting models exist making it difficult for inexperienced researchers to make an informed model selection. Forecasting enables informed and efficient responses for electricity demand. Electricity forecasting is an essential component of smart grid, which has attracted increasing academic interest.
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