title: Reinforcement learning for optimisation of a cascade river system

authors: Waddington, Nicolaas

subjects: hydropower

abstract: Getting the most out of renewable power stations has financial benefits and the environmental benefits of displacing electricity generation from fossil fuels. This thesis considers how to reduce losses in the Waikato Hydro Scheme that are due to inefficient operation while ensuring no operating constraints are breached. The hydroelectric dams are currently centrally run by a human "hydro controller" who makes decisions on how much generation to assign to each station at any given time. The hydro controller ensures no operating constraints are breached, but it may be possible to improve the running efficiency of the hydro stations. Currently, hydro controllers only have basic tools to assist them when deciding which action to take. The aim of this thesis is to create live recommendations to help them make decisions. To this end, historical data is used to train a model using reinforcement learning that can be used to provide such live recommendations on how much generation to assign to each station. The Waikato River hydro control problem is formulated as a Markov decision process and several variations on the Monte Carlo control algorithm for reinforcement learning are proposed and compared empirically. The Waikato River hydro control problem has a large action space with billions of distinct ways to assign generation to stations. The K-medoids algorithm is used to cluster actions so that 
 learning becomes practical. Furthermore, once a model has been learned, a hill-climbing approach is used to improve the quality of the selected actions. Combining these techniques gave better running efficiency than historical human performance in 18% of scenarios without breaching any operating constraints. 34% of scenarios did breach constraints so possible improvements to address this are also discussed.

text: Reinforcement learning for optimisation of a cascade river system Getting the most out of renewable power stations has financial benefits and the environmental benefits of displacing electricity generation from fossil fuels. This thesis considers how to reduce losses in the Waikato Hydro Scheme that are due to inefficient operation while ensuring no operating constraints are breached. The hydroelectric dams are currently centrally run by a human "hydro controller" who makes decisions on how much generation to assign to each station at any given time. The hydro controller ensures no operating constraints are breached, but it may be possible to improve the running efficiency of the hydro stations. Currently, hydro controllers only have basic tools to assist them when deciding which action to take. The aim of this thesis is to create live recommendations to help them make decisions. To this end, historical data is used to train a model using reinforcement learning that can be used to provide such live recommendations on how much generation to assign to each station. The Waikato River hydro control problem is formulated as a Markov decision process and several variations on the Monte Carlo control algorithm for reinforcement learning are proposed and compared empirically. The Waikato River hydro control problem has a large action space with billions of distinct ways to assign generation to stations. The K-medoids algorithm is used to cluster actions so that 
 learning becomes practical. Furthermore, once a model has been learned, a hill-climbing approach is used to improve the quality of the selected actions. Combining these techniques gave better running efficiency than historical human performance in 18% of scenarios without breaching any operating constraints. 34% of scenarios did breach constraints so possible improvements to address this are also discussed.

year: 2025

title: Online social networks: an emergent recruiter tool for attracting and screening

authors: Ollington, Nickolas

subjects: networks

abstract: Purpose – The increased popularity in using online social networks by recruiters has received much positive attention, particularly in the popular press. Using social network theory this paper aims to examine how the structure and governance mechanisms of these networks can assist this process. The authors ask: how do recruiters use online social networks to effectively attract and screen prospective job applicants? 
Design/methodology/approach – The semi-structured interview approach is used to gather data from 25 recruitment specialists. 
Findings – The connector role is identified as a specific attraction mechanism recruiters use to create numerous weak ties, where some are so weak they barely constitute ties at all. The authors then identify branding, transparency and data specificity as three mechanisms recruiters use to strengthen these ties when performing the attracting and screening functions. 
Originality/value – This is the first paper to analyse online recruitment, using social network theory, and hence it has important implications for both academic and practitioner audiences involved in recruitment.

text: Online social networks: an emergent recruiter tool for attracting and screening Purpose – The increased popularity in using online social networks by recruiters has received much positive attention, particularly in the popular press. Using social network theory this paper aims to examine how the structure and governance mechanisms of these networks can assist this process. The authors ask: how do recruiters use online social networks to effectively attract and screen prospective job applicants? 
Design/methodology/approach – The semi-structured interview approach is used to gather data from 25 recruitment specialists. 
Findings – The connector role is identified as a specific attraction mechanism recruiters use to create numerous weak ties, where some are so weak they barely constitute ties at all. The authors then identify branding, transparency and data specificity as three mechanisms recruiters use to strengthen these ties when performing the attracting and screening functions. 
Originality/value – This is the first paper to analyse online recruitment, using social network theory, and hence it has important implications for both academic and practitioner audiences involved in recruitment.

year: 2013

title: Planning and governance under the LGA: Lessons from the RMA experience.

authors: Borrie, Nancy

subjects: New Zealand

abstract: The purpose of this report is to identify ways in which experiences gained from the
RMA as a devolved and co-operative planning mandate can enable local and central
government and other stakeholders to more effectively implement the LGA. The
report is based on findings from the FRST-funded research programme on Planning
under Co-operative Mandates (PUCM). We argue in this report that the experiences gained from the RMA can inform
effective implementation of the LGA in three important respects: Preparation and implementation of LTCCPs; The community consultation process for formulating community outcomes;
and Māori participation in planning and governance.

text: Planning and governance under the LGA: Lessons from the RMA experience. The purpose of this report is to identify ways in which experiences gained from the
RMA as a devolved and co-operative planning mandate can enable local and central
government and other stakeholders to more effectively implement the LGA. The
report is based on findings from the FRST-funded research programme on Planning
under Co-operative Mandates (PUCM). We argue in this report that the experiences gained from the RMA can inform
effective implementation of the LGA in three important respects: Preparation and implementation of LTCCPs; The community consultation process for formulating community outcomes;
and Māori participation in planning and governance.

year: 2004-06-01