Combined Electricity and Traffic Short-Term Load Forecasting Using Bundled Causality Engine

Cordova, Jose; Sriram, Lalitha Madhavi Konila; Kocatepe, Ayberk; Zhou, Yuxun; Özgüven, Eren Erman; Arghandeh, Reza

doi:10.1109/tits.2018.2876871

Cited by 20 publications

(9 citation statements)

References 25 publications

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“…Our project hopes to encourage further work in this field since it was observed that the consideration of many different feature sets can achieve better aggregated results. It is important to note that related work in this field shows that the standalone concepts of similarity and causality can be effective in the prediction of energy data in various horizons [53,54], but to the best of our knowledge, there are not many available experiments that consider the combination of the two on either short-term or long-term predictions given a group of electricity clients regardless of data structure. Therefore, this work attempted to fill this research gap by providing useful insights given the scenario described in the case study.…”

Section: Discussionmentioning

confidence: 99%

A Meta-Modeling Power Consumption Forecasting Approach Combining Client Similarity and Causality

et al. 2021

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Power forecasting models offer valuable insights on the electricity consumption patterns of clients, enabling the development of advanced strategies and applications aimed at energy saving, increased energy efficiency, and smart energy pricing. The data collection process for client consumption models is not always ideal and the resulting datasets often lead to compromises in the implementation of forecasting models, as well as suboptimal performance, due to several challenges. Therefore, combinations of elements that highlight relationships between clients need to be investigated in order to achieve more accurate consumption predictions. In this study, we exploited the combined effects of client similarity and causality, and developed a power consumption forecasting model that utilizes ensembles of long short-term memory (LSTM) networks. Our novel approach enables the derivation of different representations of the predicted consumption based on feature sets influenced by similarity and causality metrics. The resulting representations were used to train a meta-model, based on a multi-layer perceptron (MLP), in order to combine the results of the LSTM ensembles optimally. This combinatorial approach achieved better overall performance and yielded lower mean absolute percentage error when compared to the standalone LSTM ensembles that do not include similarity and causality. Additional experiments indicated that the combination of similarity and causality resulted in more performant models when compared to implementations utilizing only one element on the same model structure.

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Section: Discussionmentioning

confidence: 99%

A Meta-Modeling Power Consumption Forecasting Approach Combining Client Similarity and Causality

et al. 2021

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“…In their rather innovative work, Cordova et al [47] model residential load for the city of Tallahassee combining smart electricity meter data with transport network data and use information theory and causality models for the simultaneous study of the two datasets. They compare several statistical and machine learning algorithms, such as ARIMA, MLR, LASSO and ridge regression, DNN and SVR.…”

Section: Comparing Methodsmentioning

confidence: 99%

Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations

Haben,

Arora,

Giasemidis

et al. 2021

Preprint

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The increased digitalisation and monitoring of the energy system opens up numerous opportunities to decarbonise the energy system. Applications on low voltage (LV), localised networks, such as community energy markets and smart storage will facilitate decarbonisation, but they will require advanced control and management. Reliable forecasting will be a necessary component of many of these systems to anticipate key features and uncertainties. Despite this urgent need, there has not yet been an extensive investigation into the current state-of-the-art of low voltage level forecasts, other than at the smart meter level. This paper aims to provide a comprehensive overview of the landscape, current approaches, core applications, challenges and recommendations. Another aim of this paper is to facilitate the continued improvement and advancement in this area. To this end, the paper also surveys some of the most relevant and promising trends. It establishes an open, community-driven list of the known LV level open datasets to encourage further research and development.

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“…Historic energy demand [33,34,37,39,68,71,82,89,90,101,107,108,111,131,147,163,175,178,229,236,263,285,331,340,346,349,356,361,365,396,398,425,442,449,478] Weather data [37,39,68,82,89,101,107,147,163,175,183,229,263,340,349,356,396,...…”

Section: Instance Basedmentioning

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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Combined Electricity and Traffic Short-Term Load Forecasting Using Bundled Causality Engine

Cited by 20 publications

References 25 publications

A Meta-Modeling Power Consumption Forecasting Approach Combining Client Similarity and Causality

A Meta-Modeling Power Consumption Forecasting Approach Combining Client Similarity and Causality

Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations

Modeling Energy Demand—A Systematic Literature Review

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