CHAPTER3FORECAST

In the fifth chapter the assumption is made that

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Unformatted text preview: stem. Artificial neural networks, feed-forward networks with back-propagation, are then used to estimate the forecasting model. [1, 39 – 41] 55 To conclude - except for the generation factors, all other factors are integrated in the distribution substation forecasts. The distribution substation forecasts provide important information to determine the area per sector load. With the area-based approach and taking into account the spatial load growth behaviour of the individual end-use supplies, the transmission substation load saturation points can be determined more accurately. The load growth behaviour is defined in three different growth dynamics: 1) Dormant period: The time when no load growth occurs. 2) Growth ramp: During this period growth occurs at a relatively-rapid rate, because of new construction in the area. 3) Saturated period: The area is “filled up”, fully developed. This corresponds to the three distinct phases of S-curves. The maximum transmission system load is actually the summation of all those different S-curves representing the different end-use profiles. 3.2.9 Sectors The sector load forecasts are based on sector forecasts compiled by the utility’s national key customer executives. For balancing the algorithm inputs, only the major sectors are used. For example, mining, only the sector loads for gold mines, platinum mines and coal mines are used. All other mine activities are defined as “other mining”. The grouping of sectors is in accordance with the requirements for transmission investment criteria. 3.2.10 Area per Secto r Loads The spatial load forecast divides the end-use load profiles into classes. Some of the different classes are grouped together to be aligned with the transmission investment criteria. That means that each distribution substation load is subdivided into different loads according to the grouping of the classes. All the individual grouped loads in a transmission substation supply area are summated to determine the load mix as required for the investment criteria. 56 The loads are summated again to determine the area per sector loads (see Chapter 5). The area per sector loads, the area loads and the sector loads are then checked for consensus. This is done as a matrix, where the area per sector loads are the matrix elements, the column totals are the area loads and the row totals are the sector loads. If the loads have been balanced, the column totals will equal the area loads. The row totals are compared with the sector load forecasts from the utility’s national key customer executives. The component-based approach (for load modelling) developed by EPRI divides the load into classes such as residential, commercial, industrial, agricultural, and mining. Each category of load class is represented in terms of load components such as lighting, space heating, water heating, etc. Again these end-use load profiles, divided into a number of groups (according to the investment criteria), can be used for the component-based modelling. 3.3 POINT LOADS The point loads (loads required for the load buses) are determined from the balanced results (see Chapter 5). Referring to Figure 1.4.2, the point loads are modelled and the results from the power flows are verified with the expected loads. The export and import loads are also checked to ensure that the modelled loads in the balancing algorithm are correct. 3.4 MAXIMUM AND MINIMUM LOADS The maximum and minimum loads for each area and transmission load are estimated over the last ten years and then the balanced loads are scaled to determine the expected load (see Chapter 5). 57 3.5 FACTORS CONSIDERED IN FORECASTS The next table summarises the factors that are integrated in the different forecasts X X X Generation Pattern X X X X X X T RENDS NETWORKS X X Exports X E NVIRONMENTAL System Demand Power Station X TECHNOLOGICAL S ECTORS GENERATION LOAD PROFILES ASPECTS FORECASTS REGULATORY Table 3.5.1 – Summary of Factors X X X Tx Losses A rea X X X X X X Tx Substation X X X X X X X X Dx Substation X X X X X X X X X X X Sector Area per Sector X X X X X X X 3.6 MULTIPLE REGRESSION AND NEURAL NETWORKS Quantitative forecasting methods involve the analysis of historical data in an attempt to predict future values of a variable (area loads) that is of interest. Quantitative forecasting methods can be grouped into two kinds - time series analysis and causal techniques. The use of causal forecasting models (such as multiple regression) involves the identification of other variables that are related to the variable to be predicted. Once these related variables have been identified, a statistical model describing the relationship between these variables and the variable to be forecast is developed. The statistical relationship derived is then used to forecast the variable of interest. The area demands may be related to a 58 number of political and economic indicators, etc. In that case, the area demands would be referred to as the dependen...
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This document was uploaded on 03/04/2014.

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