Mid-term load forecasting - application in oil facilities
Oil production is a strategic business, important investments in infrastructure are performed in order to assure the reliability, quality and safety of the associated electric power system. This issue requires making an accurate forecast of power demand in the medium and long term, to avoid over- or under-investments that would produce economic effects. Conventional methods of forecasting demand for industrial sectors do not consider the variables involved in the process of oil extraction. For that reason, the present article, proposes a comprehensive method to forecast the electric power demand for an oil field in the medium-term. The key drivers of the demand, are parameters related to oil production process, which are directly obtained through field measurements, e.g. oil, gas, and water production volumes. A multiple linear regression method is applicated to forecast the future electricity demand, and it is validated by comparing the results of applying a conventional regression model. Satisfactory results are obtained.
A novel short-term load forecasting approach using Adaptive Neuro-Fuzzy Inference System
By developments on smart grid technologies, the management of the grid issue get more importance. Load forecasting is one of the most important topics for managing the grid. In this study, it is aimed to develop a novel Adaptive Neuro-Fuzzy Inference System (ANFIS) based load forecasting approach for small regions having less consumption. For this aim Çay vocational high school campus area of Afyon Kocatepe University is considered. Measured and recorded hourly load demand of the campus is predicted using proposed ANFIS based model. In proposed forecasting model, only consumption parameter is required. The other parameters are calculated from this parameter. In this scope, first order derivative of the consumption, actual consumption, month and hour parameters are used as the inputs of ANFIS model to forecast next hour load demand. The experimental results show that the proposed approach is well in load forecasting for small regions.
Characterization of a tube-in-shell thermosyphon heat exchanger for solar water heating
Performance of a double-wall, natural convection, two-pass, tube-in-shell heat exchanger designed for use in a solar water heating system is measured. The heat exchanger is mounted along the length of a solar water storage tank. Flow of cold water on the shell side of the heat exchanger is driven solely by buoyancy forces. Hot side fluid is pumped. Flow rate, pressure drop and temperature data needed for a semi-empirical model of the heat exchanger are obtained. Performance is measured for forced convection, hot side volumetric flow rates of 0.016, 0.032 and 0.079 l/s. Temperature difference between hot and cold fluid at the inlets is varied from 0 to 75 degC. Measured values include thermosyphon and forced flow rates, temperature differences across the beat exchanger, vertical temperature distribution in the storage tank, and pressure drop due to shear on the thermosyphon side of the heat exchanger. Heat exchanger effectiveness and overall heat transfer area product (UA) are calculated. Thermosyphon mass flow rates are on the order of 0.01 kg/s. Although the low flow rates create thermal stratification of the water storage tank, they restrict energy transfer in the heat exchanger.