Meteorological Diversity-Load Diversity, A Fresh Look at an Old Problem

McQuigg, James D.; Johnson, Sharon R.; Tudor, J. R.

doi:10.1175/1520-0450(1972)011<0561:mdldaf>2.0.co;2

Cited by 7 publications

(5 citation statements)

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“…4 matic data had not previously been reported although, princ:ipal components have occasionally been used as independent variates to predict a dependent variate. For example, Fritts et al (9) used principal component regression to relate tree-ring growth to climatic change while McQuigg, Johnson, and Tudor (12) used the principal components of temperature to pt•edict electrical power loads.…”

Section: Methodsmentioning

confidence: 99%

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Prediction of Winter Wheat Yield From Short‐Term Weather Factors¹

Pochop¹,

Cornia²,

Becker

1975

Agronomy Journal

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Crop production is often severely limited by a lack of moisture during critical periods of the growing season. In the semi‐arid regions of the Great Plains, in particular, the time distribution of rainfall is usually thought to be as important as the total amount. Precipitation management promises one means of alleviating the problem of moisture deficiencies during critical growth periods of crops. The objective of this study was to assess the potential effects of precipitation augmentation on agricultural production in the Great Plains area of Wyoming. The influence of added rainfall on winter wheat production in eastern Wyoming has been estimated. The results are limited to winter wheat, the major crop of the region. The influence of precipitation augmentation on winter wheat production was shown to be largely dependent upon the time at which added rainfall occurs. The benefits of added rainfall to increased winter wheat production were shown to be greatest during the middle portion of the growing season, relatively negligible early in the season, and negative late in the season.

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Section: Methodsmentioning

confidence: 99%

“…Since principal components are orthogonal functions, they are readily adapted for regression analysis (15). Once the components have been calculated from the climatic data the yield data for several crops can be used for regression analysis, assuming the climatic variables included in the original data set remain unchanged (I, 7,9,II,12,15).…”

Section: Methodsmentioning

confidence: 99%

Prediction of Winter Wheat Yield From Short‐Term Weather Factors¹

Pochop¹,

Cornia²,

Becker

1975

Agronomy Journal

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“…Generally, load diversity is a reference to the level that different electricity demand patterns affect overall system demand. The level of diversity for a group of electrical loads has been defined by a coincidence factor C [5] [6], …”

Section: Geographical Loadmentioning

confidence: 99%

Comparative study on load forecasting technologies for different geographical distributed loads

Fan

Lee

et al. 2011

2011 IEEE Power and Energy Society General Meeting

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For a power system covering large geographical area, a single forecasting model for the entire region cannot guarantee the satisfactory forecasting accuracy. One of the major reasons is because the load diversity and weather diversity throughout the region. For such a system, multi-region load forecasting will be a feasible and effective solution to generate more accurate forecasting results. However, some technical issues arise when performing the multi-region load forecasting, the major challenge is how to optimally partition/combine the regions to achieve better forecasting results, especially under transient weather conditions. On the other hand, load forecasting for small areas, especially for a distribution feeder or micro grid, is also difficult because load variation in local areas is larger than that of a large system. In addition, the correlation between weather variables and small area loads would be unstable. Therefore, a two-stage load forecasting module could be utilized to improve the forecasting accuracy, and the risk assessment of local load forecasting uncertainty could be studied.This paper discusses respectively a large geographical load forecasting in Midwest US and a small area load forecasting in a UK distribution feeder. For the load forecasting at the large geographical area, a multi-region forecasting system that can find the optimal region partition in both stationary and transient weather and load conditions is discussed. For the load forecasting at the small feeder, a two-stage combination module is discussed; furthermore, risk evaluation technologies based on time-domain and frequency-domain methods are also proposed to assess the uncertainty of load forecasting.

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“…Load diversity can result in different areas having noncoincident load peaks. This diversity can be partly due to the existence of weather diversity throughout wide area of a power system [5]. For a system covering a large geographic area, the load diversity will have a large influence on the aggregated load forecasting.…”

Section: Load Diversity Analysismentioning

confidence: 99%

“…In the determination of The equation can also be rewritten as (5) It is clear that the weights on the candidate forecasts are updated after each additional observation. Also we can see that this method has a Bayesian interpretation, the weights…”

Section: Combining Temperature Forecastingmentioning

confidence: 99%

Multi-region load forecasting considering alternative meteorological predictions

Fan

Methaprayoon²,

Lee

2010

IEEE PES General Meeting

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Electricity load forecasting is always a key instrument for the effective operation and planning of power systems. This paper presents our recent works on short-term electricity demand forecasting for an electric utility in Midwest US focusing on day-ahead operation and market. The target system covers a large geographical area, and several alternative meteorological forecasts are available from different commercial weather services.For a system with large geographical area, a single model for load forecasting of the entire area sometimes cannot guarantee satisfactory forecasting accuracy because of the load diversity. We therefore develop a multi-region load forecasting model, which can find the optimal region partition under diverse weather and load conditions and finally achieve more accurate forecasts for aggregated system demand. Furthermore, to effectively take advantage of the alternative meteorological predictions in the load forecasting system, combining forecasting using adaptive coefficients is applied to share the strength of the different temperature forecasts. The proposed forecasting system has been tested by using the real data from the system. A range of comparisons with different forecasting models have been conducted. The forecasting results demonstrate the superiority of the proposed methodology. Shu Fan (M'08-SM'09) received the B.S., M.S., and Ph.D. degrees in the

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Meteorological Diversity-Load Diversity, A Fresh Look at an Old Problem

Cited by 7 publications

References 0 publications

Prediction of Winter Wheat Yield From Short‐Term Weather Factors¹

Prediction of Winter Wheat Yield From Short‐Term Weather Factors¹

Comparative study on load forecasting technologies for different geographical distributed loads

Multi-region load forecasting considering alternative meteorological predictions

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Meteorological Diversity-Load Diversity, A Fresh Look at an Old Problem

Cited by 7 publications

References 0 publications

Prediction of Winter Wheat Yield From Short‐Term Weather Factors1

Prediction of Winter Wheat Yield From Short‐Term Weather Factors1

Comparative study on load forecasting technologies for different geographical distributed loads

Multi-region load forecasting considering alternative meteorological predictions

Contact Info

Product

Resources

About

Prediction of Winter Wheat Yield From Short‐Term Weather Factors¹

Prediction of Winter Wheat Yield From Short‐Term Weather Factors¹