Clustering analysis for classification and forecasting of solar irradiance in Durban, South Africa.
| dc.contributor.advisor | Matthews, Alan Peter. | |
| dc.contributor.advisor | Brooks, Michael John. | |
| dc.contributor.author | Govender, Paulene. | |
| dc.date.accessioned | 2018-10-23T12:48:04Z | |
| dc.date.available | 2018-10-23T12:48:04Z | |
| dc.date.created | 2017 | |
| dc.date.issued | 2017 | |
| dc.description | Doctor of Philosophy. University of KwaZulu-Natal, Durban. 2017. | en_US |
| dc.description.abstract | Classification and forecasting of solar irradiance patterns has become increasingly important for operating and managing grid-connected solar power plants. A powerful approach for classification of irradiance patterns is by clustering of daily profiles, where a profile is defined as irradiance as a function of time. Classification is useful for forecasting because if the class of a day can be successfully forecast, then the irradiance profile of that day will share the general pattern of the class. In Durban, South Africa (29.871 °S; 30.977 °E), beam and diffuse irradiance profiles were recorded over a one-year period and normalized to a clear sky model to reduce the effect of seasonality, from which several variables were derived, namely minute-resolution beam, hourly-resolution beam and diffuse, and hourly-resolution beam variability. To these variables, individually and in combination, k-means clustering was applied, and beam irradiance was found to be the one that best distinguishes between sky conditions. In particular, clustering of hourly-resolution beam irradiance produced four classes with diurnal patterns characterized as sunny all day, cloudy all day, sunny morning-cloudy afternoon, and cloudy morning-sunny afternoon. These classes were then used to forecast beam and diffuse irradiance for the day ahead, in association with cloud cover forecasts from Numerical Weather Prediction (NWP) output. Two forecasting methods were investigated. The first used k-means clustering on predicted daily cloud cover percentage profiles from the NWP, which was a novel aspect of this research. The second used a rule whereby predicted cloud cover profiles were classified according to whether their averages in the morning and afternoon were above or below 50%. From both methods, four classes were obtained that had diurnal patterns associated with the irradiance classes, and these were used to forecast the irradiance class for the day ahead. The two methods had a comparable success rate of about 65%. In addition, hour-ahead forecasts of beam and diffuse irradiance were performed by using the mean profile of the forecast irradiance class to extrapolate from the current measured value to the next hour. The method showed an average improvement of about 22% for beam and diffuse irradiance over persistence forecasts. These results suggest that classification of predicted cloud cover and irradiance profiles are potentially useful for development of class-specific, multi-hour irradiance forecast models. | en_US |
| dc.identifier.uri | http://hdl.handle.net/10413/15726 | |
| dc.language.iso | en_ZA | en_US |
| dc.subject.other | Power. | en_US |
| dc.subject.other | Sun. | en_US |
| dc.subject.other | Energy. | en_US |
| dc.subject.other | Measurement. | en_US |
| dc.title | Clustering analysis for classification and forecasting of solar irradiance in Durban, South Africa. | en_US |
| dc.type | Thesis | en_US |