Electricity Consumption Modelling: A Case of Germany

Catherine, Linh Phuong; Lin, Kuan-Heng; Molnár, Péter

doi:10.2139/ssrn.2729269

Cited by 5 publications

(6 citation statements)

References 40 publications

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“…Energy consumption is related to industrial production and GDP. Electricity consumption is therefore of particular interest to business-cycle analysis (Arora and Shi, 2016;Do, Lin, and Molnár, 2016). For the most recent observations, the series is subject to unreliable data delivery as some network providers do not always provide data immediately.…”

Section: Electricity Consumptionmentioning

confidence: 99%

Economic Analysis Using Higher Frequency Time Series: Challenges for Seasonal Adjustment

Ollech

2021

SSRN Journal

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Research QuestionFrom the start of the COVID-19 pandemic, more timely and granular information on the state of the economy was sought after. To this end, indices based on higher frequency time series have been produced in order to arrive at faster and more precise assessments of economic developments. The series used in the construction of these economic indices often present methodological challenges that differ substantially from those encountered in lower frequency data. We analyse the characteristics of higher frequency time series that cause these methodological challenges in the context of seasonal adjustment. ContributionWe analyse a characteristic set of higher frequency time series that are used as inputs to the weekly activity index (WAI) published by the Deutsche Bundesbank since the beginning of the COVID-19 pandemic. The contribution of this paper is twofold. First, we seasonally adjust daily truck toll data, hourly electricity consumption data and weekly Google Trends data on unemployment. We discuss which differences to lower frequency time series are relevant for seasonal adjustment in general and in light of the COVID-19 pandemic. Second, we develop a taxonomy of the central features of seasonal higher frequency time series. ResultsThe identified central features can be clustered into three groups of characteristics: basic time series characteristics, periodic and calendar effects, and outliers and missing values. This list of features contributes to the evaluation of the seasonal adjustment of higher frequency time series and should inform the development of quality diagnostics. Nichttechnische ZusammenfassungFragestellung Mit Beginn der COVID-19 Pandemie stieg der Bedarf an frühzeitigeren und detaillierteren Informationen zur gesamtwirtschaftlichen Lage. Daher wurden verschiedene Indizes entwickelt, die höherfrequente Zeitreihen nutzen, um die gesamtwirtschaftliche Lage schneller und präziser einschätzen zu können. Die Nutzung der diesen Indizes zugrundeliegenden Zeitreihen ist allerdings mit methodischen Herausforderungen verbunden, die sich substanziell von denen bei niedrigerfrequenten Zeitreihen unterschieden. Wir analysieren die Charakteristika von höherfrequenten Zeitreihen, die die Ursache für solche methodischen Herausforderungen im Rahmen der Saisonbereinigung sind. BeitragWir untersuchen einige typische höherfrequente Zeitreihen, die seit Beginn der COVID-19 Pandemie Eingang in den Wöchentlichen Aktivitätsindex (WAI) der Deutschen Bundesbank finden. Der Beitrag dieser Untersuchung liegt erstens in der Saisonbereinigung täglicher LKW-Maut-Fahrleistungsdaten, stündlicher Energieverbrauchsdaten und wöchentlicher Google Trends Daten zur Arbeitslosigkeit. Wir zeigen dabei, welche Unterschiede zu niedrigerfrequenten Zeitreihen für die Saisonbereinigung allgemein und vor dem Hintergrund der COVID-19 Pandemie relevant sind. Zweitens entwickeln wir eine Taxonomie der zentralen Eigenschaften von saisonalen höherfrequenten Zeitreihen. ErgebnisseDie identifizierten zentralen Eigenschaften lassen ...

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Section: Electricity Consumptionmentioning

confidence: 99%

Economic Analysis Using Higher Frequency Time Series: Challenges for Seasonal Adjustment

Ollech

2021

SSRN Journal

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“…Note that we consider weekly periods based on the assumption that the correlation between the electricity consumed on the same day of a week over weeks (e.g., between Sundays) is likely to be higher than those between different days of the week (e.g., between Sundays and Mondays). As such, the consumption patterns of a typical household will likely differ on different days of the week [10,51], as their activities may differ and be particular to these days. Consequently, when computing the consumption profile of a household, it becomes necessary to consider its typical consumption pattern during each day of the week [51].…”

Section: Modelling Households As Agentsmentioning

confidence: 99%

“…This utility is a mapping from the consumption of agents. 10 Comparison between agents is possible because all agents' comfort values are on the same scale of to 1, not minding how much the electricity they demand (or consume) differ. 11 Incentive compatible mechanisms are known to reward agents which act according to their preferences with the best outcomes for such actions [42].…”

Section: The Fair Load Shedding Problem (Flsp)mentioning

confidence: 99%

Solving the fair electric load shedding problem in developing countries

Oluwasuji

Malik

Zhang

et al. 2019

Auton Agent Multi-Agent Syst

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Often because of limitations in generation capacity of power stations, many developing countries frequently resort to disconnecting large parts of the power grid from supply, a process termed load shedding. This leaves households in disconnected parts without electricity, causing them inconvenience and discomfort. Without fairness being taken into due consideration during load shedding, some households may suffer more than others. In this paper, we solve the fair load shedding problem (FLSP) by creating solutions which connect households to supply based on some fairness criteria (i.e., to fairly connect homes to supply in terms of duration, their electricity needs, and their demand), which we model as their utilities. First, we briefly describe some state-of-art household-level load shedding heuristics which meet the first criteria. Second, we model the FLSP as a resource allocation problem, which we formulate into two Mixed Integer Programming (MIP) problems based on the Multiple Knapsack Problem. In so doing, we use the utilitarian, egalitarian and envy-freeness social welfare metrics to develop objectives and constraints that ensure our FLSP solutions results in fair allocations that consider the utilities of agents. Then, we solve the FLSP and show that our MIP models maximize the groupwise and individual utilities of agents, and minimize the differences between their pairwise utilities under a number of experiments. When taken together, our endeavour establishes a set of benchmarks for fair load shedding schemes, and provide insights for designing fair allocation solutions for other scarce resources.

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“…Clements ve diğerleri gün içindeki yarım saatlik periyotlarla enerji tüketimi verilerini kullanarak zaman serileri yaklaşımıyla tahmin modeli geliştirmiştir [9]. Bir başka çalışma [10], saatlik enerji tüketimi verilerinin kullanımının daha doğru tahmin sağladığı sonucuna ulaşmıştır.…”

Section: Gi̇ri̇ş (Introduction)unclassified

Yapay Sinir Ağları ve Zaman Serileri Yöntemi ile Bir Endüstri Alanının (İvedik OSB) Elektrik Enerjisi İhtiyaç Tahmini

Özden¹,

Öztürk²

2018

Bilişim Teknolojileri Dergisi

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Özet-Özel firmalara enerji sağlayan firmalar veya işletmeler için enerji tüketiminin tahmini ve ihtiyaç planlaması çok kritiktir. Özellikle endüstri bölgelerindeki enerji ihtiyacı ev kullanıcılarının ihtiyacından daha yüksektir, bundan dolayı enerji ihtiyacının doğru tahminini gerektirir. Bu çalışmada, zaman serileri ve yapay sinir ağları olmak üzere iki farklı yaklaşım kullanılarak Türkiye'deki bir endüstri bölgesi için enerji ihtiyaç tahmini üzerinde çalışılmış ve sonuçlar test edilmiştir. Daha önceki çalışmalardan farklı olarak, kısıtlı veri ile kısa dönem tahmini için basit bir model geliştirilmiştir. Model, giriş parametresi olarak geçmiş günlere ait tüketim verileri ve sıcaklığı içermektedir. Sıcaklık verisi, endüstri bölgelerinde ısıtma amaçlı enerji tüketiminde kullanıldığı için anahtar rol oynamaktadır. Zaman serileri yaklaşımında sadece geçmişe ait enerji tüketim verileri kullanılmıştır. Her iki yaklaşım enerji ihtiyaç tahmininde kullanılmış, sonuçlar tartışılmış ve karşılaştırılmıştır. Elde edilen sonuçlara göre; zaman serileri yöntemi R değeri 0.93901, yapay sinir ağları yöntemi R değeri 0.9859 elde edilmiştir. Zaman serileri yöntemi veri kısıtlığı sebebiyle yapay sinir ağlarına göre daha kötü bir tahmin gerçekleştirmiştir.

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Electricity Consumption Modelling: A Case of Germany

Cited by 5 publications

References 40 publications

Economic Analysis Using Higher Frequency Time Series: Challenges for Seasonal Adjustment

Economic Analysis Using Higher Frequency Time Series: Challenges for Seasonal Adjustment

Solving the fair electric load shedding problem in developing countries

Yapay Sinir Ağları ve Zaman Serileri Yöntemi ile Bir Endüstri Alanının (İvedik OSB) Elektrik Enerjisi İhtiyaç Tahmini

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