Different data sources, data types and platforms are involved in modelling emissions load profiles. In our case, we model emission load profiles at the regional or city level. However, we found missing values, redundancy and inconsistency in the datasets, and in most cases data preprocessing is unavoidable. Data pre-processing converts the data into a clean and tidy dataset for the subsequent modelling steps or statistical analyses. Therefore, some common techniques for data pre-processing such as cleaning, transformation, integration, reduction and some terms in data mining such as filtering and selection have been applied in our case study. We usually do the data pre-processing of moderate problems and a small amount of data using a spreadsheet application, whereas we use the programming language to do the more complex and big data size tasks. As a result, it has been found that understanding the nature of our data collection, the data flow process and the desired output comprehensively is the key for efficiency in data pre-processing. The applied techniques have helped us to provide the proper input for modelling the regional emission load profile efficiently.
The introduction of smart meters and time-use survey data is helping decision makers to understand the residential electricity consumption behaviour behind load profiles. However, it can be difficult to obtain the actual detailed consumption data due to privacy issues. Synthesising residential electricity consumption profiles may be an alternative way to develop synthetic load profiles that initially starts by reviewing the existing synthetic load profile methods. The purpose of this review is to identify the recent methods for synthesising residential electricity load profiles by conducting a rigorous standalone literature review. This review study has been applied and presented transparently and is replicable by other researchers. The review has answered the following research questions: the definition, concept and roles of residential electricity load profile and synthesised data; recent approaches and methods; research purposes; applicable simulations and validation methods of the final selected studies. The results show that the most applied approach in modelling residential electricity load profiles is the bottom-up approach. As it is detailed, it suitable to reflect the local residential behaviour in electricity consumption. Consequently, it is more complex to develop and calibrate the model as identified in the results.Bottom-up models are more powerful in analysing energy consumptions that focus on behavioural patterns, dwelling profiles and control strategies.
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