bibliography.bib

  ##############
  # R packages

  @Manual{baseR,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2016},
    url = {https://www.R-project.org/},
  }
 @Book{bookdown,
    title = {bookdown: Authoring Books and Technical Documents with {R} Markdown},
    author = {Yihui Xie},
    publisher = {Chapman and Hall/CRC},
    address = {Boca Raton, Florida},
    year = {2016},
    note = {ISBN 978-1138700109},
    url = {https://github.com/rstudio/bookdown},
  }
  @Manual{data.table,
    title = {data.table: Extension of Data.frame},
    author = {M Dowle and A Srinivasan and T Short and S Lianoglou with contributions from R Saporta and E Antonyan},
    year = {2015},
    note = {R package version 1.9.6},
    url = {https://CRAN.R-project.org/package=data.table},
  }
  @Manual{dplyr,
    title = {dplyr: A Grammar of Data Manipulation},
    author = {Hadley Wickham and Romain Francois},
    year = {2016},
    note = {R package version 0.5.0},
    url = {https://CRAN.R-project.org/package=dplyr},
  }
   @Book{ggplot2,
    author = {Hadley Wickham},
    title = {ggplot2: Elegant Graphics for Data Analysis},
    publisher = {Springer-Verlag New York},
    year = {2009},
    isbn = {978-0-387-98140-6},
    url = {http://ggplot2.org},
  }

  @Article{ggmap,
    author = {David Kahle and Hadley Wickham},
    title = {ggmap: Spatial Visualization with ggplot2},
    journal = {The R Journal},
    year = {2013},
    volume = {5},
    number = {1},
    pages = {144--161},
    url = {http://journal.r-project.org/archive/2013-1/kahle-wickham.pdf},
  }
 @Manual{GREENGridData,
    title = {GREENGridData: Processing NZ GREEN Grid project data to create a 'safe' version for data archiving and re-use},
    author = {Ben Anderson and David Eyers},
    year = {2018},
    note = {R package version 1.0},
    url = {https://github.com/CfSOtago/GREENGridData},

  }
  @Manual{gridExtra,
    title = {gridExtra: Miscellaneous Functions for "Grid" Graphics},
    author = {Baptiste Auguie},
    year = {2016},
    note = {R package version 2.2.1},
    url = {https://CRAN.R-project.org/package=gridExtra},
  }
  @Manual{here,
    title = {here: A Simpler Way to Find Your Files},
    author = {Kirill Müller},
    year = {2017},
    note = {R package version 0.1},
    url = {https://CRAN.R-project.org/package=here},
  }
  @Manual{hms,
    title = {hms: Pretty Time of Day},
    author = {Kirill Müller},
    year = {2018},
    note = {R package version 0.4.2},
    url = {https://CRAN.R-project.org/package=hms},
  }
  @Manual{kableExtra,
    title = {kableExtra: Construct Complex Table with 'kable' and Pipe Syntax},
    author = {Hao Zhu},
    year = {2018},
    note = {R package version 0.8.0},
    url = {https://CRAN.R-project.org/package=kableExtra},
  }
  @Manual{knitr,
    title = {knitr: A General-Purpose Package for Dynamic Report Generation in R},
    author = {Yihui Xie},
    year = {2016},
    url = {https://CRAN.R-project.org/package=knitr},
  }
   @Article{lubridate,
    title = {Dates and Times Made Easy with {lubridate}},
    author = {Garrett Grolemund and Hadley Wickham},
    journal = {Journal of Statistical Software},
    year = {2011},
    volume = {40},
    number = {3},
    pages = {1--25},
    url = {http://www.jstatsoft.org/v40/i03/},
  }
  @Manual{progress,
    title = {progress: Terminal Progress Bars},
    author = {Gábor Csárdi and Rich FitzJohn},
    year = {2016},
    note = {R package version 1.1.2},
    url = {https://CRAN.R-project.org/package=progress},
  }
  @Manual{readr,
    title = {readr: Read Tabular Data},
    author = {Hadley Wickham and Jim Hester and Romain Francois},
    year = {2016},
    note = {R package version 1.0.0},
    url = {https://CRAN.R-project.org/package=readr},
  }
  @Manual{readxl,
    title = {readxl: Read Excel Files},
    author = {Hadley Wickham and Jennifer Bryan},
    year = {2017},
    note = {R package version 1.0.0},
    url = {https://CRAN.R-project.org/package=readxl},
  }
  @Manual{rmarkdown,
    title = {rmarkdown: Dynamic Documents for R},
    author = {JJ Allaire and Yihui Xie and Jonathan McPherson and Javier Luraschi and Kevin Ushey and Aron Atkins and Hadley Wickham and Joe Cheng and Winston Chang},
    year = {2018},
    note = {R package version 1.10},
    url = {https://CRAN.R-project.org/package=rmarkdown},
  }
  @Article{reshape2,
    title = {Reshaping Data with the {reshape} Package},
    author = {Hadley Wickham},
    journal = {Journal of Statistical Software},
    year = {2007},
    volume = {21},
    number = {12},
    pages = {1--20},
    url = {http://www.jstatsoft.org/v21/i12/},
  }
  @Manual{skimr,
    title = {skimr: skimr},
    author = {Eduardo {Arino de la Rubia} and Hao Zhu and Shannon Ellis and Elin Waring and Michael Quinn},
    year = {2017},
    note = {R package version 1.0},
    url = {https://github.com/ropenscilabs/skimr},
  }
   @Manual{stringr,
    title = {stringr: Simple, Consistent Wrappers for Common String Operations},
    author = {Hadley Wickham},
    year = {2016},
    note = {R package version 1.1.0},
    url = {https://CRAN.R-project.org/package=stringr}
  }

##############
# Papers

@article{stephenson_smart_2017,
	title = {Smart grid research in {New} {Zealand}–{A} review from the {GREEN} {Grid} research programme},
	journal = {Renewable and Sustainable Energy Reviews},
	author = {Stephenson, Janet and Ford, Rebecca and Nair, Nirmal-Kumar and Watson, Neville and Wood, Alan and Miller, Allan},
	year = {2017},
	url = {https://doi.org/10.1016/j.rser.2017.07.010},
	pages = {1636--1645},
	volume = {82 (1)}
}

@report{GREENGridTotalLoad,
  author = {Ben Anderson},
  title = {GREENGrid Household Electricity Demand Data Test: Imputed total power demand using: circuitsToSum_v1.1},
  institution = {University of Otago: Centre for Sustainability},
  year = {2019},
  url = {https://cfsotago.github.io/GREENGridData/reportTotalPower_circuitsToSum_v1.1.html}
}

@article{simpson_combining_2005,
	title = {Combining sample and census data in small area estimates: {Iterative} {Proportional} {Fitting} with standard software},
	volume = {57},
	number = {2},
	journal = {The Professional Geographer},
	author = {Simpson, L and Tranmer, M},
	year = {2005},
	pages = {222--234}
}

@incollection{anderson_estimating_2012,
	address = {London},
	title = {Estimating {Small} {Area} {Income} {Deprivation}: {An} {Iterative} {Proportional} {Fitting} {Approach}},
	copyright = {All rights reserved},
	booktitle = {Spatial {Microsimulation}: {A} {Reference} {Guide} for {Users}},
	publisher = {Springer},
	author = {Anderson, Ben},
	editor = {Edwards, Kimberley and Tanton, R},
	year = {2012}
}


@inproceedings{anderson_small_2019,
	address = {Christchurch, Aotearoa New Zealand},
	title = {Small {Area} {Temporal} {Household} {Electricity} {Demand} {Microsimulation} {Models} for {New} {Zealand}: {Why}, how and how far have we got?},
	author = {Anderson, Ben},
	month = feb,
	year = {2019}
}


@mastersthesis{dortans_estimating_2019,
	address = {University of Otago: Dunedin},
	title = {Estimating the {Technical} {Potential} for {Residential} {Household} {Appliances} to {Reduce} {Daily} {Peak} {Electricity} {Demand} in {New} {Zealand}},
	school = {Centre for Sustainability},
	author = {Dortans, Carsten},
	month = mar,
	year = {2019}
}


@article{mckenna_high-resolution_2016,
	title = {High-resolution stochastic integrated thermal–electrical domestic demand model},
	volume = {165},
	issn = {0306-2619},
	url = {http://www.sciencedirect.com/science/article/pii/S0306261915016621},
	doi = {10.1016/j.apenergy.2015.12.089},
	abstract = {This paper describes the extension of CREST’s existing electrical domestic demand model into an integrated thermal–electrical demand model. The principle novelty of the model is its integrated structure such that the timing of thermal and electrical output variables are appropriately correlated. The model has been developed primarily for low-voltage network analysis and the model’s ability to account for demand diversity is of critical importance for this application. The model, however, can also serve as a basis for modelling domestic energy demands within the broader field of urban energy systems analysis. The new model includes the previously published components associated with electrical demand and generation (appliances, lighting, and photovoltaics) and integrates these with an updated occupancy model, a solar thermal collector model, and new thermal models including a low-order building thermal model, domestic hot water consumption, thermostat and timer controls and gas boilers. The paper reviews the state-of-the-art in high-resolution domestic demand modelling, describes the model, and compares its output with three independent validation datasets. The integrated model remains an open-source development in Excel VBA and is freely available to download for users to configure and extend, or to incorporate into other models.},
	urldate = {2016-01-12},
	journal = {Applied Energy},
	author = {McKenna, Eoghan and Thomson, Murray},
	month = mar,
	year = {2016},
	keywords = {Energy use, Domestic, Energy demand model, High-resolution, Stochastic, Thermal demand},
	pages = {445--461},
	file = {ScienceDirect Full Text PDF:/Users/ben/Zotero/storage/3R4M6S67/McKenna and Thomson - 2016 - High-resolution stochastic integrated thermal–elec.pdf:application/pdf;ScienceDirect Snapshot:/Users/ben/Zotero/storage/V83C2EIV/S0306261915016621.html:text/html;ScienceDirect Snapshot:/Users/ben/Zotero/storage/EPD9BEAK/McKenna and Thomson - 2016 - High-resolution stochastic integrated thermal–elec.html:text/html}
}

@article{mckenna_simulating_2017,
	title = {Simulating residential demand response: {Improving} socio-technical assumptions in activity-based models of energy demand},
	issn = {1570-646X, 1570-6478},
	shorttitle = {Simulating residential demand response},
	url = {https://link.springer.com/article/10.1007/s12053-017-9525-4},
	doi = {10.1007/s12053-017-9525-4},
	abstract = {Demand response is receiving increasing interest as a new form of flexibility within low-carbon power systems. Energy models are an important tool to assess the potential capability of demand side contributions. This paper critically reviews the assumptions in current models and introduces a new conceptual framework to better facilitate such an assessment. We propose three dimensions along which change could occur, namely technology, activities and service expectations. Using this framework, the socio-technical assumptions underpinning ‘bottom-up’ activity-based energy demand models are identified and a number of shortcomings are discussed. First, links between appliance usage and activities are not evidence-based. We propose new data collection approaches to address this gap. Second, aside from thermal comfort, service expectations, which can be an important source of flexibility, are under-represented and their inclusion into demand models would improve their predicative power in this area. Finally, flexibility can be present over a range of time scales, from immediate responses, to longer term trends. Longitudinal time use data from participants in demand response schemes may be able to illuminate these. The recommendations of this paper seek to enhance the current state-of-the-art in activity-based models and to provide useful tools for the assessment of demand response.},
	language = {en},
	urldate = {2017-05-16},
	journal = {Energy Efficiency},
	author = {McKenna, Eoghan and Higginson, Sarah and Grunewald, Philipp and Darby, Sarah J.},
	month = may,
	year = {2017},
	pages = {1--15}
}

@techreport{ec2Survey2015,
  title = {National Household Survey of Energy and Transportation: Energy Cultures Two},
  url = {http://hdl.handle.net/10523/5634},
  school = {University of Otago},
  address = {Centre for Sustainability},
  author = {Wooliscroft, B},
  year = {2015}
}

@article{jack_minimal_2018,
	title = {A minimal simulation of the electricity demand of a domestic hot water cylinder for smart control},
	volume = {211},
	journal = {Applied Energy},
	author = {Jack, M. W. and Suomalainen, K. and Dew, J. J. W. and Eyers, D.},
	year = {2018},
	url = {https://www.sciencedirect.com/science/article/pii/S0306261917316197},
	pages = {104--112}
}
@mastersthesis{dianaThesis2015,
  title = {Developing an energy-related Time-Use Diary for gaining insights into New Zealand households’ electricity consumption},
  url = {http://hdl.handle.net/10523/5957},
  school = {University of Otago},
  address = {Centre for Sustainability},
  author = {Giraldo Ocampo, Diana},
  year = {2015}
}
@article{suomalainen_detailed_2019,
	title = {Detailed comparison of energy-related time-use diaries and monitored residential electricity demand},
	volume = {183},
	doi = {10.1016/j.enbuild.2018.11.002},
	journal = {Energy and Buildings},
	author = {Suomalainen, Kiti and Eyers, David and Ford, Rebecca and Stephenson, Janet and Anderson, Ben and Jack, Michael},
	year = {2019},
	pages = {418--427}
}


@article{anderson_ensuring_2020,
	title = {Ensuring statistics have power: {Guidance} for designing, reporting and acting on electricity demand reduction and behaviour change programs},
	volume = {59},
	issn = {2214-6296},
	shorttitle = {Ensuring statistics have power},
	url = {http://www.sciencedirect.com/science/article/pii/S2214629619303780},
	doi = {10.1016/j.erss.2019.101260},
	abstract = {In this paper we address ongoing confusion over the meaning of statistical significance and statistical power in energy efficiency and energy demand reduction intervention studies. We discuss the role of these concepts in designing studies, in deciding what can be inferred from the results and thus what course of subsequent action to take. We do this using a worked example of a study of Heat Pump demand response in New Zealand to show how to appropriately size experimental and observational studies, the consequences this has for subsequent data analysis and the decisions that can then be taken. The paper then provides two sets of recommendations. The first focuses on the uncontroversial but seemingly ignorable issue of statistical power analysis and sample design, something regularly omitted in the energy studies literature. The second focuses on how to report energy demand reduction study or trial results, make inferences and take commercial or policy-oriented decisions in a contextually appropriate way. The paper therefore offers guidance to researchers tasked with designing and assessing such studies; project managers who need to understand what can count as evidence, for what purpose and in what context and decision makers who need to make defensible commercial or policy decisions based on that evidence. The paper therefore helps all of these stakeholders to distinguish the search for statistical significance from the requirement for actionable evidence and so avoid throwing the substantive baby out with the p-value bathwater.},
	urldate = {2019-10-15},
	journal = {Energy Research \& Social Science},
	author = {Anderson, Ben and Rushby, Tom and Bahaj, Abubakr and James, Patrick},
	month = jan,
	year = {2020},
	keywords = {Energy studies, Sample size, Statistical power, Statistical significance, Study design},
	pages = {101260}
}
@techreport{vicki_white_warm_2017,
	address = {Porirua, New Zealand},
	title = {Warm, dry, healthy? {Insights} from the 2015 {House} {Condition} {Survey} on insulation, ventilation, heating and mould in {New} {Zealand} houses},
	number = {SR 372},
	institution = {BRANZ Ltd},
	author = {{Vicki White} and {Mark Jones}},
	year = {2017}
}
@techreport{white_branz_2017,
	address = {Porirua, New Zealand},
	title = {{BRANZ} 2015 {House} {Condition} {Survey}: {Comparison} of house condition by tenure},
	shorttitle = {{BRANZ} 2015 {House} {Condition} {Survey}},
	institution = {BRANZ},
	author = {White, Vicki and Jones, Mark and Cowan, Vicki and Chun, Saera},
	year = {2017}
}


@article{huebner_explaining_2016,
	title = {Explaining domestic energy consumption - {The} comparative contribution of building factors, socio-demographics, behaviours and attitudes},
	volume = {159},
	issn = {03062619},
	url = {https://www.sciencedirect.com/science/article/pii/S0306261916305360},
	doi = {10.1016/j.apenergy.2016.04.075},
	abstract = {This paper tests to what extent different types of variables (building factors, socio-demographics, attitudes and self-reported behaviours) explain annualized energy consumption in residential buildings, and goes on to show which individual variables have the highest explanatory power. In contrast to many other studies, the problem of multicollinearity between predictors is recognised, and addressed using Lasso regression to perform variable selection. Using data from a sample of 924 English households collected in 2011/12, four individual regression models showed that building variables on their own explained about 39\% of the variability in energy consumption, socio-demographic variables 24\%, heating behaviour 14\% and attitudes \& other behaviours only 5\%. However, a combined model encompassing all predictors explained only 44\% of all variability, indicating a significant extent of multicollinearity between predictors. Once corrected for multicollinearity, building variables predominantly remained as significant predictors of energy consumption, in particular the dwelling's size and type. Of the sociodemographic predictors, only the household size remained significant, and of the heating behaviours only the length of heating season was significant. Reported beliefs about climate change were also a significant predictor. The findings indicate that whilst people use energy, it is physical building characteristics that largely determine how much is used. This finding, together with the relatively greater time-invariant nature of building characteristics underlines their importance when focusing on seeking to understand residential energy consumption at a stock level. Retrofitting and behaviour change initiatives remain important avenues to reduce consumption, as suggested through the lower energy consumption associated with full double-glazing and shorter heating season. However, the dominance of building size also indicates that living in appropriately sized buildings is of great importance for energy consumption. The results also indicate that more than half of the variability in energy consumption cannot be explained, even when using a wide range of predictors. The paper also discusses the need to collect better occupant-related variables to give a correct representation of the impact of behaviour, such as heating demand temperatures. Furthermore, choices about dwelling characteristics could also be seen as a type of behaviour, even though it cannot be modelled in a cross-sectional analysis as used in this study.},
	journal = {Applied Energy},
	author = {Huebner, Gesche M. and Hamilton, Ian and Chalabi, Zaid and Shipworth, David and Oreszczyn, Tadj},
	month = sep,
	year = {2016},
	pages = {692--702},
	file = {Attachment:/Users/ben/Zotero/storage/3H7UHP29/Huebner et al. - Applied Energy - 2015 - Explaining domestic energy consumption - The comparative contribution of building factors, soci.pdf:application/pdf}
}


@article{battaglia_sampling_2016,
	title = {Sampling, data collection, and weighting procedures for address-based sample surveys},
	volume = {4},
	number = {4},
	journal = {Journal of Survey Statistics and Methodology},
	author = {Battaglia, Michael P. and Dillman, Don A. and Frankel, Martin R. and Harter, Rachel and Buskirk, Trent D. and McPhee, Cameron B. and DeMatteis, Jill M. and Yancey, Tracey},
	year = {2016},
	pages = {476--500}
}

@article{simpson_combining_2005,
	title = {Combining sample and census data in small area estimates: {Iterative} {Proportional} {Fitting} with standard software},
	volume = {57},
	number = {2},
	journal = {The Professional Geographer},
	author = {Simpson, L and Tranmer, M},
	year = {2005},
	pages = {222--234}
}

@article{wong_reliability_1992,
	title = {The {Reliability} of {Using} the {Iterative} {Proportional} {Fitting} {Procedure}∗},
	url = {http://www.tandfonline.com/doi/abs/10.1111/j.0033-0124.1992.00340.x},
	journal = {The Professional Geographer},
	author = {Wong, DWS},
	year = {1992}
}


@incollection{birkin_spatial_2011,
	address = {London},
	title = {Spatial {Microsimulation} {Models}: {A} {Review} and a {Glimpse} into the {Future}},
	booktitle = {Population {Dynamics} and {Projection} {Methods}},
	publisher = {Springer},
	author = {Birkin, M and Clarke, M},
	editor = {Stillwell, J and Clarke, M},
	year = {2011}
}


@article{casati_synthetic_2015,
	title = {Synthetic population generation by combining a hierarchical, simulation-based approach with reweighting by generalized raking},
	volume = {2493},
	number = {1},
	journal = {Transportation Research Record},
	author = {Casati, Daniele and Müller, Kirill and Fourie, Pieter J. and Erath, Alexander and Axhausen, Kay W.},
	year = {2015},
	pages = {107--116}
}


@article{beaujouan_reweighting_2011,
	title = {Reweighting the general household survey 1979–2007},
	volume = {145},
	number = {1},
	journal = {Population Trends},
	author = {Beaujouan, Éva and Brown, James J. and Bhrolcháin, Máire Ní},
	year = {2011},
	pages = {119--145}}


@article{ford_emerging_2017,
	title = {Emerging energy transitions: {PV} uptake beyond subsidies},
	volume = {117},
	shorttitle = {Emerging energy transitions},
	journal = {Technological Forecasting and Social Change},
	author = {Ford, Rebecca and Walton, Sara and Stephenson, Janet and Rees, David and Scott, Michelle and King, Geoff and Williams, John and Wooliscroft, Ben},
	year = {2017},
	pages = {138--150}
}


@article{sovacool_promoting_2018,
	title = {Promoting novelty, rigor, and style in energy social science: {Towards} codes of practice for appropriate methods and research design},
	issn = {2214-6296},
	shorttitle = {Promoting novelty, rigor, and style in energy social science},
	url = {http://www.sciencedirect.com/science/article/pii/S2214629618307230},
	doi = {10.1016/j.erss.2018.07.007},
	abstract = {A series of weaknesses in creativity, research design, and quality of writing continue to handicap energy social science. Many studies ask uninteresting research questions, make only marginal contributions, and lack innovative methods or application to theory. Many studies also have no explicit research design, lack rigor, or suffer from mangled structure and poor quality of writing. To help remedy these shortcomings, this Review offers suggestions for how to construct research questions; thoughtfully engage with concepts; state objectives; and appropriately select research methods. Then, the Review offers suggestions for enhancing theoretical, methodological, and empirical novelty. In terms of rigor, codes of practice are presented across seven method categories: experiments, literature reviews, data collection, data analysis, quantitative energy modeling, qualitative analysis, and case studies. We also recommend that researchers beware of hierarchies of evidence utilized in some disciplines, and that researchers place more emphasis on balance and appropriateness in research design. In terms of style, we offer tips regarding macro and microstructure and analysis, as well as coherent writing. Our hope is that this Review will inspire more interesting, robust, multi-method, comparative, interdisciplinary and impactful research that will accelerate the contribution that energy social science can make to both theory and practice.},
	urldate = {2018-10-15},
	journal = {Energy Research \& Social Science},
	author = {Sovacool, Benjamin K. and Axsen, Jonn and Sorrell, Steve},
	month = oct,
	year = {2018},
	keywords = {Validity, Interdisciplinary research, Research excellence, Research methodology, Research methods}
}

@techreport{electricityInNZ_2018,
	type = {Annual {Report}},
	title = {Electricity in {New} {Zealand} 2018},
	url = {https://www.ea.govt.nz/about-us/media-and-publications/electricity-new-zealand/},
	language = {en},
	urldate = {2018-02-09},
	institution = {Electricity Authority},
	author = {{Electricity Authority/Te Mana Hiko}},
	year = {2018}
}


@article{whitworth_estimating_2017,
	title = {Estimating uncertainty in spatial microsimulation approaches to small area estimation: {A} new approach to solving an old problem},
	volume = {63},
	shorttitle = {Estimating uncertainty in spatial microsimulation approaches to small area estimation},
	journal = {Computers, Environment and Urban Systems},
	author = {Whitworth, Adam and Carter, E. and Ballas, Dimitris and Moon, G.},
	year = {2017},
	pages = {50--57}
}


@article{tanton_spatial_2018,
	title = {Spatial {Microsimulation}: {Developments} and {Potential} {Future} {Directions}},
	volume = {11},
	shorttitle = {Spatial {Microsimulation}},
	number = {1},
	journal = {International Journal of Microsimulation},
	author = {Tanton, Robert},
	year = {2018},
	pages = {143--161}
}