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In this article, a systematic literature review of 419 articles on energy demand modeling, published between 2015 and 2020, is presented. This provides researchers with an exhaustive overview of the examined literature and classification of techniques for energy demand modeling. Unlike in existing literature reviews, in this comprehensive study all of the following aspects of energy demand models are analyzed: techniques, prediction accuracy, inputs, energy carrier, sector, temporal horizon, and spatial granularity. Readers benefit from easy access to a broad literature base and find decision support when choosing suitable data-model combinations for their projects. Results have been compiled in comprehensive figures and tables, providing a structured summary of the literature, and containing direct references to the analyzed articles. Drawbacks of techniques are discussed as well as countermeasures. The results show that among the articles, machine learning (ML) techniques are used the most, are mainly applied to short-term electricity forecasting on a regional level and rely on historic load as their main data source. Engineering-based models are less dependent on historic load data and cover appliance consumption on long temporal horizons. Metaheuristic and uncertainty techniques are often used in hybrid models. Statistical techniques are frequently used for energy demand modeling as well and often serve as benchmarks for other techniques. Among the articles, the accuracy measured by mean average percentage error (MAPE) proved to be on similar levels for all techniques. This review eases the reader into the subject matter by presenting the emphases that have been made in the current literature, suggesting future research directions, and providing the basis for quantitative testing of hypotheses regarding applicability and dominance of specific methods for sub-categories of demand modeling.
In this article, a systematic literature review of 419 articles on energy demand modeling, published between 2015 and 2020, is presented. This provides researchers with an exhaustive overview of the examined literature and classification of techniques for energy demand modeling. Unlike in existing literature reviews, in this comprehensive study all of the following aspects of energy demand models are analyzed: techniques, prediction accuracy, inputs, energy carrier, sector, temporal horizon, and spatial granularity. Readers benefit from easy access to a broad literature base and find decision support when choosing suitable data-model combinations for their projects. Results have been compiled in comprehensive figures and tables, providing a structured summary of the literature, and containing direct references to the analyzed articles. Drawbacks of techniques are discussed as well as countermeasures. The results show that among the articles, machine learning (ML) techniques are used the most, are mainly applied to short-term electricity forecasting on a regional level and rely on historic load as their main data source. Engineering-based models are less dependent on historic load data and cover appliance consumption on long temporal horizons. Metaheuristic and uncertainty techniques are often used in hybrid models. Statistical techniques are frequently used for energy demand modeling as well and often serve as benchmarks for other techniques. Among the articles, the accuracy measured by mean average percentage error (MAPE) proved to be on similar levels for all techniques. This review eases the reader into the subject matter by presenting the emphases that have been made in the current literature, suggesting future research directions, and providing the basis for quantitative testing of hypotheses regarding applicability and dominance of specific methods for sub-categories of demand modeling.
Unsustainable use of electricity has severe implications on the environment and human well-being. With an estimated consumption of about 20% of total global electricity demand, the household sector is a key player in efforts for crafting interventions for reducing electricity consumption. Despite increasing calls for behavioural solutions to electricity conservation at the household level, more attention has been paid to technical than behavioural interventions. Yet a deeper understanding of electricity use behaviour is needed to design interventions and engender integration of behavioural interventions into demand-side management and decision making. Although South Africa is energy insecure and a major greenhouse gas emitter, less attention has been paid to household electricity use using behavioural lenses. Using a scoping review approach, this study inductively reviewed publications to examine the state of research on household electricity use in South Africa, focussing on (1) research trends and contexts, (2) conceptual focus, (3) proposed interventions for reducing electricity consumption and (4) future research needs. Very few publications considered reported and actual electricity use behaviour. Most publications (65%) paid attention to technical dimensions for reducing household electricity consumption such as economic nudges and technical retrofits, rather than behavioural strategies. Of the publications that focussed on behaviour, very few explicitly examined reported electricity use behaviour. Most publications did not consider the role of partnerships in designing interventions for reducing electricity consumption but rather employed individualistic perspectives. Overall, the results suggest that calls for behaviour change research have not been fully heeded. More studies on electricity use behaviour in different contexts, including across an income heterogeneity gradient, and the role of context dependent collective settings in drafting interventions, are required to better inform pathways to sustainable electricity use.
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