Households’ Electricity Consumption in Hungarian Urban Areas

Bakó, Ferenc; Berkes, Judit; Szigeti, Cecília

doi:10.3390/en14102899

Cited by 7 publications

(8 citation statements)

References 81 publications

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“…About half of those multi-city studies focused on several cities within one country. For example, one study analyzed 46 small and medium-sized towns in Hungary [33], another 291 cities across Brazil [34], and one study focused on small cities on Madagascar [35]. We also found nine records that covered cities in multiple countries within the same world region, such as in East Asia [36] or South America [37].…”

Section: A Global Research Landscape Across 250 Citiesmentioning

confidence: 91%

A systematic map of machine learning in urban climate change mitigation

Hintz,

Milojevic-Dupont,

Creutzig

et al. 2024

Preprint

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To tackle the climate crisis, cities must reduce greenhouse gas (GHG) emissions rapidly. To aid these efforts, many cities are interested in leveraging artificial intelligence and machine learning (ML). Researchers and practitioners, however, only begin to understand how ML can contribute to achieving climate targets in urban contexts. To provide an overview of application areas, and the potential leverage of ML to reduce GHG emissions, we systematically map research conducted over the past three decades. We identify 1,206 relevant peer-reviewed records, and discover that research involving ML is expanding more rapidly than the literature on urban climate mitigation more broadly. The research focus largely aligns with urban mitigation options that the Intergovernmental Panel on Climate Change assessed as having high impact. We also find that research concentrates on the ML-strong regions Eastern Asia, Europe, and Northern America. This regional focus can influence research agendas, and we observed signs that this can lead to bias regarding which ML applications are pursued in urban climate action.

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Section: A Global Research Landscape Across 250 Citiesmentioning

confidence: 91%

A systematic map of machine learning in urban climate change mitigation

Hintz,

Milojevic-Dupont,

Creutzig

et al. 2024

Preprint

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“…Peak electricity consumption for industry is proxied by daily maximum electricity power [65][66][67] and the industrial production index. Electricity consumption in households can be also driven by daily maximum electricity power, cross-border transmission grid prices, monthly gross salary and gross domestic product as a macro-economic variable of the economic cycle [68][69][70][71][72].…”

Section: Hypothesis (H3)mentioning

confidence: 99%

Electricity Markets during the Liberalization: The Case of a European Union Country

Bojnec

Križaj

2021

Energies

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This paper analyzes electricity markets in Slovenia during the specific period of market deregulation and price liberalization. The drivers of electricity prices and electricity consumption are investigated. The Slovenian electricity markets are analyzed in relation with the European Energy Exchange (EEX) market. Associations between electricity prices on the one hand, and primary energy prices, variation in air temperature, daily maximum electricity power, and cross-border grid prices on the other hand, are analyzed separately for industrial and household consumers. Monthly data are used in a regression analysis during the period of Slovenia’s electricity market deregulation and price liberalization. Empirical results show that electricity prices achieved in the EEX market were significantly associated with primary energy prices. In Slovenia, the prices for daily maximum electricity power were significantly associated with electricity prices achieved on the EEX market. The increases in electricity prices for households, however, cannot be explained with developments in electricity prices on the EEX market. As the period analyzed is the stage of market deregulation and price liberalization, this can have important policy implications for the countries that still have regulated and monopolized electricity markets. Opening the electricity markets is expected to increase competition and reduce pressures for electricity price increases. However, the experiences and lessons learned among the countries following market deregulation and price liberalization are mixed. For industry, electricity prices affect cost competitiveness, while for households, electricity prices, through expenses, affect their welfare. A competitive and efficient electricity market should balance between suppliers’ and consumers’ market interests. With greening the energy markets and the development of the CO2 emission trading market, it is also important to encourage use of renewable energy sources.

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“…In this landscape, buildings, whether commercial skyscrapers or residential complexes are significant energy consumers. Bakó et al [2] have documented the electricity consumption patterns of urban households in Hungary, highlighting the importance of understanding regional and temporal variations in energy consumption [2]. Such studies highlight the complexity of electricity consumption in buildings and the importance of accurate forecasting models.…”

Section: Introductionmentioning

confidence: 99%

Electricity Consumption (kW) Forecast for a Building of Interest Based on a Time Series Nonlinear Regression Model

Omogoroye,

Olaniyi,

Adebiyi

et al. 2023

AJEBA

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This paper investigates the relationship between a building's past energy consumption and the outdoor temperature and predicts the next day's energy consumption using a refined time series model. Maintaining optimal indoor temperatures relative to outdoor temperatures determines a building's HVAC demand and, thus, energy consumption. We want to determine how outdoor temperature and other factors determine this consumption. With increasing urbanization and energy demand, it is important to understand building energy consumption, especially in terms of its impact on the environment. Previous research has shown the link between electricity consumption and external environmental factors and highlighted energy optimization's importance in urban structures. As cities become large energy consumers, studies point to the need to understand energy use patterns on a regional and temporal scale. For accurate energy forecasts, data becomes the linchpin. Time series—data points arranged in chronological intervals—are foundational in predictive modeling. Due to buildings' intricate electricity consumption patterns, traditional linear forecasting often falls short. Enter nonlinear regression models: These complex models are apt for mapping and predicting nonlinear data trends. Notwithstanding their advantages, they come with challenges, primarily the high-frequency data influx from smart meters and IoT devices. But their potential benefits - from cost savings to efficient energy management - are significant. In a world caught between urban expansion and ecological preservation, efficient energy management is crucial. Accurate energy forecasting, especially for buildings, combines technological advances, statistical acumen and environmental imperatives. Understanding building energy consumption using sophisticated nonlinear regression models is evolving from an academic goal to a global necessity.

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Households’ Electricity Consumption in Hungarian Urban Areas

Cited by 7 publications

References 81 publications

A systematic map of machine learning in urban climate change mitigation

A systematic map of machine learning in urban climate change mitigation

Electricity Markets during the Liberalization: The Case of a European Union Country

Electricity Consumption (kW) Forecast for a Building of Interest Based on a Time Series Nonlinear Regression Model

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