Uncertainty management in electricity demand forecasting with machine learning and ensemble learning: Case studies of COVID-19 in the US metropolitans

Baker, Mohammed Rashad; Jihad, Kamal H.; Al-bayaty, Hussein; Ghareeb, Ahmed; Ali, Hessein; Choi, Jun-Ki; Sun, Qiancheng

doi:10.1016/j.engappai.2023.106350

Cited by 20 publications

(5 citation statements)

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“…The evolution of prediction techniques used in non-intrusive load monitoring (NILM) has been driven by advancements in machine learning and data analysis [13][14][15]. Various machine learning algorithms such as artificial neural network (ANN), support vector machines (SVM), k-Nearest Neighbor (k-NN), hidden Markov model (HMM), decision tree (DT), random forest (RF), and deep learning (DL) have been utilized for load disaggregation methods based on pattern recognition in NILM.…”

Section: Literature Surveymentioning

confidence: 99%

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Resilient data-driven non-intrusive load monitoring for efficient energy management using machine learning techniques

Nutakki,

Mandava

2024

EURASIP J. Adv. Signal Process.

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The integration of smart homes into smart grids presents numerous challenges, particularly in managing energy consumption efficiently. Non-intrusive load management (NILM) has emerged as a viable solution for optimizing energy usage. However, as smart grids incorporate more distributed energy resources, the complexity of demand-side management and energy optimization escalates. Various techniques have been proposed to address these challenges, but the evolving grid necessitates intelligent optimization strategies. This article explores the potential of data-driven NILM (DNILM) by leveraging multiple machine learning algorithms and neural network architectures for appliance state monitoring and predicting future energy consumption. It underscores the significance of intelligent optimization techniques in enhancing prediction accuracy. The article compares several data-driven mechanisms, including decision trees, sequence-to-point models, denoising autoencoders, recurrent neural networks, long short-term memory, and gated recurrent unit models. Furthermore, the article categorizes different forms of NILM and discusses the impact of calibration and load division. A detailed comparative analysis is conducted using evaluation metrics such as root-mean-square error, mean absolute error, and accuracy for each method. The proposed DNILM approach is implemented using Python 3.10.5 on the REDD dataset, demonstrating its effectiveness in addressing the complexities of energy optimization in smart grid environments.

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Section: Literature Surveymentioning

confidence: 99%

“…() can be achieved by implementing the prediction methodology to forecast the θ i s . The predicted output for the ith appliance for T time sequences with optimization in error is given as (15) (10)…”

Section: Co Algorithmmentioning

confidence: 99%

Resilient data-driven non-intrusive load monitoring for efficient energy management using machine learning techniques

Nutakki,

Mandava

2024

EURASIP J. Adv. Signal Process.

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“…Qualitative evaluation methods primarily focus on subjective judgments, such as peer review (Franzoni & Stephan, 2023), expert judgment (Dahanayake et al, 2003), case studies (Baker et al, 2023;Merisalo-Rantanen et al, 2005), and surveys/interviews (Chang et al, 2023;Reale et al, 2007;Van den Besselaar & Sandstrom, 2016). The peer review method involves inviting domain experts to assess the quality of a research project.…”

Section: Research Project Evaluation Approachesmentioning

confidence: 99%

A Machine Learning and Large Language Model-Integrated Approach to Research Project Evaluation

Ma,

Zheng,

Zhu

et al. 2024

Journal of Database Management

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Research project evaluation upon completion is one of the important tasks for research management in government funding agencies and research institutions. Due to the increased number of funded projects, it is hard to find qualified reviewers in the same research disciplines. This paper proposes a machine learning and large language model integrated approach to provide decision support for research project evaluation. Machine learning algorithms are proposed to compute the weights of key performance indicators (KPIs) and scores of KPIs based on the evaluation results of completed projects, large language models are used to summarize research contributions or findings on project reports. Then domain experts are invited to consolidate the weights and scores for the KPIs and assess the novelty and impact of research contribution or findings. Experiments have been conducted in practical settings and the results have shown that the proposed method can greatly improve research management efficiency and provide more consistent evaluation results on funded research projects.

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“…In contrast, the gradient boosting trees model shows an RMSE of 203 and MAE of 154 MWh, compared to an ARIMA model with 226 and 173 MWh, respectively. Moreover, methods for predicting load using LSTM and genetic algorithms providing a comparison of the ML algorithms are implemented in [41,42], LSTM for univariate household energy forecasting in [43], ensemble learning approach for demand forecasting in [44] and LSTM for predicting wind generation in [45]. Additionally, a survey of LSTM and related models in wind energy predictive analytics was provided [46].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms for Power System Sign Classification and a Multivariate Stacked LSTM Model for Predicting the Electricity Imbalance Volume

Bâra,

Oprea

2024

Int J Comput Intell Syst

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The energy transition to a cleaner environment has been a concern for many researchers and policy makers, as well as communities and non-governmental organizations. The effects of climate change are evident, temperatures everywhere in the world are getting higher and violent weather phenomena are more frequent, requiring clear and firm pro-environmental measures. Thus, we will discuss the energy transition and the support provided by artificial intelligence (AI) applications to achieve a cleaner and healthier environment. The focus will be on applications driving the energy transition, the significant role of AI, and collective efforts to improve societal interactions and living standards. The price of electricity is included in almost all goods and services and should be affordable for the sustainable development of economies. Therefore, it is important to model, anticipate and understand the trend of electricity markets. The electricity price includes an imbalance component which is the difference between notifications and real-time operation. Ideally it is zero, but in real operation such differences are normal due to load variation, lack of renewable energy sources (RES) accurate prediction, unplanted outages, etc. Therefore, additional energy has to be produced or some generating units are required to reduce generation to balance the power system. Usually, this activity is performed on the balancing market (BM) by the transmission system operator (TSO) that gathers offers from generators to gradually reduce or increase the output. Therefore, the prediction of the imbalance volume along with the prices for deficit and surplus is of paramount importance for producers’ decision makers to create offers on the BM. The main goal is to predict the imbalance volume and minimize the costs that such imbalance may cause. In this chapter, we propose a method to predict the imbalance volume based on the classification of the imbalance sign that is inserted into the dataset for predicting the imbalance volume. The imbalance sign is predicted using several classifiers and the output of the classification is added to the input dataset. The rest of the exogenous variables are shifted to the values from previous day d − 1. Therefore, the input variables are either predicted (like the imbalance sign) or are known from d − 1. Several metrics, such as mean average percentage error (MAPE), determination coefficient R2 and mean average error (MAE) are calculated to assess the proposed method of combining classification machine learning (ML) algorithms and recurrent neural networks (RNN) that memorize variations, namely long short-term memory (LSTM) model.

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Uncertainty management in electricity demand forecasting with machine learning and ensemble learning: Case studies of COVID-19 in the US metropolitans

Cited by 20 publications

References 60 publications

Resilient data-driven non-intrusive load monitoring for efficient energy management using machine learning techniques

Resilient data-driven non-intrusive load monitoring for efficient energy management using machine learning techniques

A Machine Learning and Large Language Model-Integrated Approach to Research Project Evaluation

Machine Learning Algorithms for Power System Sign Classification and a Multivariate Stacked LSTM Model for Predicting the Electricity Imbalance Volume

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