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Second, the relation between wind data and energy production is determined via a five parameter logistic function using actual high-frequency energy production data.
It is to be noted that measured wind speed frequency distribution data are from April 2011 to March 2012 whereas measured wind turbine-9 energy production data are from April 2012 to March 2013.
To predict the energy production, data from many weather stations are required, and a new forecasting strategy is proposed for weather related parameters (solar irradiance, wind speed, and ambient temperature).
In this section we introduce necessary theoretical background on convection in flat and tilted porous media which are analogous to brine saturated sandstone geothermal reservoirs used in this study as well as describe the numerical simulation model used to obtain thermal energy production data.
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The results show that integrating energy with production data enables factories to provide specific energy consumption information for decision makers at the factory level, as well as for the consumers and the regulators.
A pilot study was carried out to integrate electrical energy data, production data and scheduling data in real time to achieve the multi-level awareness of energy used in production.
For energy production, metrological data (wind speed, solar irradiance…) and steady-state models have been used.
This integration of energy and production data is achieved efficiently when there is a high level of standardization of production processes and the availability of detailed energy usage data.
Advanced analysis of the energy production and consumption data and data-driven decision support can be combined to promote the formation and development of smart energy systems.
We compare the results of the model with real energy production obtained from data available in the Lastem station (Italy) and sampled every 10 min.
Other energy production technologies had data on the supply of the resource that were categorical (i.e., ordinal scale).
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