Unified weekly peak load forecasting for fast growing power system

Elrazaz, Z.; Mazi, A. A.

doi:10.1049/ip-c.1989.0005

Cited by 12 publications

(2 citation statements)

References 6 publications

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“…Numerous statistical techniques have been developed for modeling elasticities, including time series and regression models such as multiple regression [36,37], Autoregressive Integrated Moving Average (ARIMA) models [38,39], Vector Autoregressive (VAR)/Vector Error-Correction (VEC) models [40][41][42], and Autoregressive Distributed Lag (ADL)/Error-Correction Model (ECM) models [43,44]. However, insufficient observations make it challenging to estimate long-term elasticities and obtain accurate and reliable results using time series models.…”

Section: Methodology and Datamentioning

confidence: 99%

Price and Income Elasticities of Hungarian Household Energy Demand: Implications for Energy Policy in the Context of the Energy Crisis

Szép,

Kashour

2023

GLCE

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The energy crisis that began in the second half of 2021, exacerbated by the Russian-Ukrainian war, created unexpected difficulty for European Union Member States in terms of shaping their energy policies. Hungary was one of the most vulnerable countries, whose economic development was fraught with severe risk due to the strong dependence on Russian energy sources and regulated energy prices for households, significantly slowing down the transition to green energy. The sharp change in energy prices due to the introduction of a price cap in 2013-2014 and the partial adjustment in 2022 draws attention to the situation of Hungarian energy demand. This study describes the absolute short-term price elasticities of Hungarian household natural gas and electricity demand and gas and electricity cross-price and income elasticities of the former for income deciles between 2010 and 2021 using the midpoint percentage method. The results show that electricity demand is more elastic than gas demand, implying that Hungarian household consumers are more responsive to changes in electricity prices than those of gas. In addition, low-income Hungarian households are more sensitive to changes in both of the latter than high-income households, while no consistent pattern is identified in the relationship between income and energy demand. Accordingly, the study recommends the implementation of a multi-tariff pricing strategy based on the energy burden of the income deciles. This should particularly target the first two deciles, which are the most vulnerable and sensitive to energy price changes.

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Section: Methodology and Datamentioning

confidence: 99%

Price and Income Elasticities of Hungarian Household Energy Demand: Implications for Energy Policy in the Context of the Energy Crisis

Szép,

Kashour

2023

GLCE

View full text Add to dashboard Cite

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“…To this end, different techniques have been proposed, presenting triple exponential smoothing [ 26 ], decomposition methods [ 27 , 28 ], or multiple equation time series [ 29 ]. Similarly, the maximum weekly load consumption is forecasted for a one-year horizon in [ 30 ], where the different components of the decomposed load are modeled by ARIMAX and ARIMA models. These models incorporate previous forecasting errors in the regression equation and thus outperform the simpler AR models in general.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Model Ensemble for Mixed Power Load Forecasting across Multiple Time Horizons

Γιαμαρέλος

Papadimitrakis

Stogiannos

et al. 2023

Sensors

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The increasing penetration of renewable energy sources tends to redirect the power systems community’s interest from the traditional power grid model towards the smart grid framework. During this transition, load forecasting for various time horizons constitutes an essential electric utility task in network planning, operation, and management. This paper presents a novel mixed power-load forecasting scheme for multiple prediction horizons ranging from 15 min to 24 h ahead. The proposed approach makes use of a pool of models trained by several machine-learning methods with different characteristics, namely neural networks, linear regression, support vector regression, random forests, and sparse regression. The final prediction values are calculated using an online decision mechanism based on weighting the individual models according to their past performance. The proposed scheme is evaluated on real electrical load data sensed from a high voltage/medium voltage substation and is shown to be highly effective, as it results in R2 coefficient values ranging from 0.99 to 0.79 for prediction horizons ranging from 15 min to 24 h ahead, respectively. The method is compared to several state-of-the-art machine-learning approaches, as well as a different ensemble method, producing highly competitive results in terms of prediction accuracy.

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Proposal and analysis of cavity-backed slot antennas for microwave energy transmission

Hikage

Ohno

Omiya

et al. 1999

Electron. Comm. Jpn. Pt. I

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A cavity‐backed slot antenna is thought to be one of the most suitable elements for the wireless transmission of microwave energy, and especially for SPS2000, which was conceptually developed in Japan as a straw‐man model of the solar power satellite. First, this paper presents the configuration and dimensions of the antenna element. Second, we propose a feeder geometry for which the above requirements are fulfilled and the desired antenna characteristics are obtained. The feeders are a short‐ended probe and an inverted‐F element whose dimensional parameters are examined experimentally and analytically. Here we use the FDTD technique developed in our previous study for the analysis of the cavity‐backed slot antenna excited by these two kinds of feeder. © 1998 Scripta Technica, Electron Comm Jpn Pt 1, 82(3): 20–27, 1999

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Unified weekly peak load forecasting for fast growing power system

Cited by 12 publications

References 6 publications

Price and Income Elasticities of Hungarian Household Energy Demand: Implications for Energy Policy in the Context of the Energy Crisis

Price and Income Elasticities of Hungarian Household Energy Demand: Implications for Energy Policy in the Context of the Energy Crisis

A Machine Learning Model Ensemble for Mixed Power Load Forecasting across Multiple Time Horizons

Proposal and analysis of cavity-backed slot antennas for microwave energy transmission

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