Electricity Consumption Prediction for Xinjiang Electric Energy Replacement

Song, Xiaojing; Liang, Gang; Chang-zu, LI; Chen, Weiwei

doi:10.1155/2019/3262591

Cited by 7 publications

(7 citation statements)

References 18 publications

(19 reference statements)

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“…Both FARX(p) models in (17) or (18) corresponding to having functional or scalar exogenous variables, respectively, are the generalization of the model discussed in (16) with different numbers of functional or scalar exogenous variables. e estimation method discussed in Section 3.4.2 is modified and summarized as follows:…”

Section: T (τ) − μ(T)� 􏽘mentioning

confidence: 99%

Functional Data Approach for Short-Term Electricity Demand Forecasting

Shah

Jan

Ali

2022

Mathematical Problems in Engineering

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In today’s liberalized electricity markets, modeling and forecasting electricity demand data are highly important for the effective management of the power system. However, electricity demand forecasting is a challenging task due to the specific features it exhibits. These features include the presence of extreme values, spikes or jumps, multiple periodicities, long trend, and bank holiday effect. In addition, the forecasts are required for a complete day as electricity demand is decided a day before the physical delivery. Therefore, this study aimed to investigate the forecasting performance of models based on functional data analysis, a relatively less explored area in energy research. To this end, the demand time series is first treated for the extreme values. The filtered series is then divided into deterministic and stochastic components. The generalized additive modeling technique is used to model the deterministic component, whereas functional autoregressive (FAR), FAR with exogenous variable (FARX), and classical univariate AR models are used to model and forecast the stochastic component. Data from the Nord Pool electricity market are used, and the one-day-ahead out-of-sample forecast obtained for a whole year is evaluated using different forecasting accuracy measures. The results indicate that the functional modeling approach produces superior forecasting results, while FARX outperforms FAR and classical AR models. More specifically, for the NP electricity demand, FARX produces a MAPE value of 2.74, whereas 6.27 and 9.73 values of MAPE are obtained for FAR and AR models, respectively.

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Section: T (τ) − μ(T)� 􏽘mentioning

confidence: 99%

Functional Data Approach for Short-Term Electricity Demand Forecasting

Shah

Jan

Ali

2022

Mathematical Problems in Engineering

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“…Historic energy demand [17,27,30,[61][62][63]75,[84][85][86]91,96,118,131,133,134,138,140,143,146,[148][149][150]152,154,[156][157][158][159][160]168,181,189,192,202,230,231,234,239,240,242,243,255,262,263,268,270,273,277,280,282,…”

Section: Regressionmentioning

confidence: 99%

Modeling Energy Demand—A Systematic Literature Review

et al. 2021

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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.

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“…Energy utilization efficiency and indoor thermal comfort are the main evaluation indicators for the Heating Ventilation and Air Conditioning (HVAC) system. Up to now, 40% of the world energy consumption has been consumed by building energy consumption [1]. And air conditioning accounts for around 40% of the total building energy consumption [2,3].…”

Section: Introductionmentioning

confidence: 99%

Effects of Supply Parameters of Stratum Ventilation on Energy Utilization Efficiency and Indoor Thermal Comfort: A Computational Approach

Zhang

Mao

et al. 2021

Mathematical Problems in Engineering

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Stratum ventilation shows the significant potential on energy conservation and indoor thermal comfort under cooling applications. Yet, only limited researches focus on the thermal performance of stratum ventilation under heating condition. The heating and cooling operation characteristic of stratum ventilation is different due to the distinct airflow characteristics. Therefore, this paper investigated the parameters that affect energy utilization efficiency and indoor thermal comfort under heating condition served by stratum ventilation via CFD simulations approach. The supply air parameters included temperature, airflow rate, angle, and return air outlet positions. The evaluation indicators adopt ventilation effectiveness and effective draft temperature (EDT) for assessing the energy utilization efficiency and indoor thermal comfort served by stratum ventilation under heating condition. The results demonstrated that, under the heating mode of stratum ventilation, different effects on the thermal performance were made by the mentioned parameters. The ventilation effectiveness was higher when the air supply temperature is 26°C, airflow rate is 7 air change per hour (ACH), and the air supply angle is 45°. The EDT range of the occupied zone is closest to zero K when the air supply temperature is 28°C, airflow rate is 12 (ACH), and the air supply angle is 60°. The related conclusions obtained from this study provide the theoretical basis for the stratum ventilation design and promote its heating application.

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Electricity Consumption Prediction for Xinjiang Electric Energy Replacement

Cited by 7 publications

References 18 publications

Functional Data Approach for Short-Term Electricity Demand Forecasting

Functional Data Approach for Short-Term Electricity Demand Forecasting

Modeling Energy Demand—A Systematic Literature Review

Effects of Supply Parameters of Stratum Ventilation on Energy Utilization Efficiency and Indoor Thermal Comfort: A Computational Approach

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