title: Estimation of total cloud cover from solar radiation observations at Lake Rotorua, New Zealand

authors: Luo, Liancong

subjects: clear-sky solar irradiance

abstract: The DYRESM-CAEDYM model is a valuable tool for simulating water temperature for biochemical studies in aquatic ecosystem. The model requires inputs of surface short-wave radiation and long-wave radiation or total cloud cover fraction (TC). Long-wave radiation is often not measured directly so a method to determine TC from commonly measured short-wave solar irradiance (E0) and theoretical short-wave solar irradiance under a clear sky (Ec) has broad application. A more than 17-year (15 November 1991 to 20 February 2009) hourly solar irradiance data set was used to estimate the peak solar irradiance for each ordinal date over one year, which was assumed to be representative of solar irradiance in the absence of cloud. Comparison between these daily observed values and the modelled clear-sky solar radiation over one year was in close agreement (Pearson correlation coefficient, r = 0.995 and root mean squared error, RMSE = 12.54W m-²). The downloaded hourly cloudiness measurements from 15 November 1991 to 20 February 2009 was used to calculate the daily values for this period and then the calculated daily values over the 17 years were used to calculate the average values for each ordinal date over one year. A regression equation between (1 - E0/Ec) and TC produced a correlation coefficient value of 0.99 (p > 0.01, n = 71). The validation of this cloud cover estimation model was conducted with observed short-wave solar radiation and TC at two sites. Values of TC derived from the model at the Lake Rotorua site gave a reasonable prediction of the observed values (RMSE = 0.10, r = 0.86, p > 0.01, n = 61). The model was also tested at Queenstown (South Island of New Zealand) and it provided satisfactory results compared to the measurements (RMSE = 0.16, r = 0.67, p > 0.01, n = 61). Therefore the model’s good performance and broad applicability will contribute to the DYRESM-CAEDYM accuracy of water temperature simulation when long-wave radiation is not available.

text: Estimation of total cloud cover from solar radiation observations at Lake Rotorua, New Zealand The DYRESM-CAEDYM model is a valuable tool for simulating water temperature for biochemical studies in aquatic ecosystem. The model requires inputs of surface short-wave radiation and long-wave radiation or total cloud cover fraction (TC). Long-wave radiation is often not measured directly so a method to determine TC from commonly measured short-wave solar irradiance (E0) and theoretical short-wave solar irradiance under a clear sky (Ec) has broad application. A more than 17-year (15 November 1991 to 20 February 2009) hourly solar irradiance data set was used to estimate the peak solar irradiance for each ordinal date over one year, which was assumed to be representative of solar irradiance in the absence of cloud. Comparison between these daily observed values and the modelled clear-sky solar radiation over one year was in close agreement (Pearson correlation coefficient, r = 0.995 and root mean squared error, RMSE = 12.54W m-²). The downloaded hourly cloudiness measurements from 15 November 1991 to 20 February 2009 was used to calculate the daily values for this period and then the calculated daily values over the 17 years were used to calculate the average values for each ordinal date over one year. A regression equation between (1 - E0/Ec) and TC produced a correlation coefficient value of 0.99 (p > 0.01, n = 71). The validation of this cloud cover estimation model was conducted with observed short-wave solar radiation and TC at two sites. Values of TC derived from the model at the Lake Rotorua site gave a reasonable prediction of the observed values (RMSE = 0.10, r = 0.86, p > 0.01, n = 61). The model was also tested at Queenstown (South Island of New Zealand) and it provided satisfactory results compared to the measurements (RMSE = 0.16, r = 0.67, p > 0.01, n = 61). Therefore the model’s good performance and broad applicability will contribute to the DYRESM-CAEDYM accuracy of water temperature simulation when long-wave radiation is not available.

year: 2010

title: Surrogate-Assisted Evolutionary Algorithms for Wind Farm Layout Optimisation Problem

authors: Zheng, Chen

subjects: surrogate-assisted evolutionary optimisation

abstract: Due to the increasing need for computationally expensive optimisation in many real-world applications, surrogate-assisted evolutionary algorithms have attracted growing attention. In the literature, surrogate-assisted evolutionary approaches have been successful in highly computational expensive optimisation problems. However, surrogates have not been used with the Wind Farm Layout Optimisation Problem (WFLOP) before.

In this work, an evolutionary approach using surrogate modelling techniques to reduce the computational cost of the WFLOP is studied. The WFLOP mainly focuses on finding the optimal geographical placement of wind turbines within a wind farm in order to maximise power generation. But evaluating wind farm layout is very computationally expensive. The purpose of using surrogates is to approximate the real evaluation function of an evolutionary algorithm, but the surrogates can be computed more efficiently. The aim of this study is try to discover whether if surrogate-assisted evolutionary approach is effective on the WFLOP.

An analytical wake model is used to calculate the velocity deficits in the downstream generated by individual turbines. A set of initial offline experiments was conducted based on a dataset of wind farm layouts sampled from the space of all layouts, using biased random walk. These experiments were designed to discover which features lead to construction of an accurate surrogate model. According to the results of these experiments, polar coordinates (sorted according to distance) as features are selected for learning. A multilayer perceptron (MLP) neural network and a tree-based regression model (M5P) are chosen as the surrogate models to approximate the real fitness function in conjunction with an (mu, lambda) evolutionary strategy. Two previously presented BlockCopy operators are used in the evolutionary strategy. The surrogate models are managed using a modified version of the Pre-selection strategy and the Best strategy.

Our evaluation used four benchmark wind farm scenarios with dimensionality ranging from 200 to 1420 dimensions. The evaluation results show that our preliminary MLP and M5P surrogate models did not improve the optimisation results over traditional evolutionary strategies due to scalability issues. The scalability is a known weakness of many surrogate-assisted evolutionary approaches for the reason that most of them are designed for low-dimensionality problems. However, the research should continue on this topic because of its importance to renewable energy.

text: Surrogate-Assisted Evolutionary Algorithms for Wind Farm Layout Optimisation Problem Due to the increasing need for computationally expensive optimisation in many real-world applications, surrogate-assisted evolutionary algorithms have attracted growing attention. In the literature, surrogate-assisted evolutionary approaches have been successful in highly computational expensive optimisation problems. However, surrogates have not been used with the Wind Farm Layout Optimisation Problem (WFLOP) before.

In this work, an evolutionary approach using surrogate modelling techniques to reduce the computational cost of the WFLOP is studied. The WFLOP mainly focuses on finding the optimal geographical placement of wind turbines within a wind farm in order to maximise power generation. But evaluating wind farm layout is very computationally expensive. The purpose of using surrogates is to approximate the real evaluation function of an evolutionary algorithm, but the surrogates can be computed more efficiently. The aim of this study is try to discover whether if surrogate-assisted evolutionary approach is effective on the WFLOP.

An analytical wake model is used to calculate the velocity deficits in the downstream generated by individual turbines. A set of initial offline experiments was conducted based on a dataset of wind farm layouts sampled from the space of all layouts, using biased random walk. These experiments were designed to discover which features lead to construction of an accurate surrogate model. According to the results of these experiments, polar coordinates (sorted according to distance) as features are selected for learning. A multilayer perceptron (MLP) neural network and a tree-based regression model (M5P) are chosen as the surrogate models to approximate the real fitness function in conjunction with an (mu, lambda) evolutionary strategy. Two previously presented BlockCopy operators are used in the evolutionary strategy. The surrogate models are managed using a modified version of the Pre-selection strategy and the Best strategy.

Our evaluation used four benchmark wind farm scenarios with dimensionality ranging from 200 to 1420 dimensions. The evaluation results show that our preliminary MLP and M5P surrogate models did not improve the optimisation results over traditional evolutionary strategies due to scalability issues. The scalability is a known weakness of many surrogate-assisted evolutionary approaches for the reason that most of them are designed for low-dimensionality problems. However, the research should continue on this topic because of its importance to renewable energy.

year: 2016

title: Te tukuihotanga o te reo Māori: He aronga motuhake – ngā mokopuna a Te Kapunga

authors: Dewes, Te Kuru o te Marama

subjects: Reo Māori

abstract: Ko te tāhū o tēnei rangahau ko te reo Māori. Ko te ariā matua ko te tukuihotanga o te reo Māori, arā, ko te tuku iho o te reo ki rō whānau mai i ngā mātua ki ngā tamariki, ā, tae rawa iho ki ngā mokopuna. Kia whakawhāiti i te kaupapa, he aronga motuhake tēnei kei te titiro hāngai atu ki ngā whānau e whā ki ngā tāone e toru o Te Whanganui-a-Tara, Tāmakimakaurau me Te Rotorua-Nui-a-Kahumatamomoe. Nā te hurahura i ngā āhuatanga reo i roto i ngā kāenga o ngā whānau e whā i kitea mehemea kei te tutuki tēnei tūāhua te tukuihotanga o te reo Māori. Tuia, he aha ngā momo āhuatanga kei te taunaki i tēnei mahi, ā, he aha hoki ngā ārai e whakahōtaetae ana i a ia.  piti atu, i noho ki te ketuketu i ngā whakaaro o ngā mātua e pā ana ki tēnei kaupapa te tuku iho i te reo ki ngā tamariki mokopuna. Kāre i aro ki te kounga o te reo, he kaute noa i ngā whakamahinga reo Māori me whakamahinga reo Pākehā. Kia whakaritea ngā āhuatanga reo o ngā whānau me ngā tamariki, i tū ngā mahi whakarongo ki ngā wāhi e toru, arā, ki rō kāenga, ki rō kura, ka mutu, i tū ki wāhi kē atu hoki. Nā runga i te whakaaro kei te mōrearea tonu te reo Māori, kua herea tēnei kaupapa ki te ahurea me te tuakiri Māori, te mana motuhake me te tino rangatiratanga.

text: Te tukuihotanga o te reo Māori: He aronga motuhake – ngā mokopuna a Te Kapunga Ko te tāhū o tēnei rangahau ko te reo Māori. Ko te ariā matua ko te tukuihotanga o te reo Māori, arā, ko te tuku iho o te reo ki rō whānau mai i ngā mātua ki ngā tamariki, ā, tae rawa iho ki ngā mokopuna. Kia whakawhāiti i te kaupapa, he aronga motuhake tēnei kei te titiro hāngai atu ki ngā whānau e whā ki ngā tāone e toru o Te Whanganui-a-Tara, Tāmakimakaurau me Te Rotorua-Nui-a-Kahumatamomoe. Nā te hurahura i ngā āhuatanga reo i roto i ngā kāenga o ngā whānau e whā i kitea mehemea kei te tutuki tēnei tūāhua te tukuihotanga o te reo Māori. Tuia, he aha ngā momo āhuatanga kei te taunaki i tēnei mahi, ā, he aha hoki ngā ārai e whakahōtaetae ana i a ia.  piti atu, i noho ki te ketuketu i ngā whakaaro o ngā mātua e pā ana ki tēnei kaupapa te tuku iho i te reo ki ngā tamariki mokopuna. Kāre i aro ki te kounga o te reo, he kaute noa i ngā whakamahinga reo Māori me whakamahinga reo Pākehā. Kia whakaritea ngā āhuatanga reo o ngā whānau me ngā tamariki, i tū ngā mahi whakarongo ki ngā wāhi e toru, arā, ki rō kāenga, ki rō kura, ka mutu, i tū ki wāhi kē atu hoki. Nā runga i te whakaaro kei te mōrearea tonu te reo Māori, kua herea tēnei kaupapa ki te ahurea me te tuakiri Māori, te mana motuhake me te tino rangatiratanga.

year: 2017