Influencing Factors Evaluation of Machine Learning-Based Energy Consumption Prediction

Kim, Yong-Jun; Byun, Yung-Cheol; Lee, Sang-Joon

doi:10.3390/en14217167

Cited by 20 publications

(14 citation statements)

References 36 publications

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“…In [10], researchers proposed machine learning techniques to improve the accuracy of predicting energy consumption. This work analyses the overall prediction using error curve learning and a hybrid model using consumption data of South Korea.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning for Exploration of the Factors Affecting the Building Energy Usage: A Case Study

Asif¹,

Saba

2023

ACE

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This paper presents a thorough exploratory analysis of one of the data research tracks involving the building energy usage data of different cities of France. To gain a deep understanding of the dataset and the various attributes that comprise the data, we performed an exploratory data analysis and used data visualization to study the different variables and the correlation that exists between them. This was done to determine which factors really contribute to having a huge effect on the energy (gas) consumption leading to climate change. In our study, we also predicted the level of its consumption using a Decision-tree based algorithm and Light GBM, which was found to give satisfactory results.

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Section: Related Workmentioning

confidence: 99%

Machine Learning for Exploration of the Factors Affecting the Building Energy Usage: A Case Study

Asif¹,

Saba

2023

ACE

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“…Digital twins of energy assets 13 [9][10][11][12][13][14][15][16][17][18][19][20][21] Energy forecasting 14 [1,[22][23][24][25][26][27][28][29][30][31][32][33][34] Optimization and coordination 18 [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52] VPP applications in smart grids Energy services delivery 31 [4,5,22,35,38, Local energy autonomy 21 [5,…”

Section: Vpp Concepts and Technologymentioning

confidence: 99%

“…The stochastic nature of renewable energy generation and volatility of energy prices are limiting factors for VPP participation in energy markets [1]. Therefore, it is important to improve the quality of the forecasting models and techniques to lower as much as possible the impact of these uncertain parameters on the optimization problem [23].…”

Section: Energy Forecastingmentioning

confidence: 99%

Virtual Power Plant Optimization in Smart Grids: A Narrative Review

2022

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Virtual power plants (VPPs) are promising solutions to address the decarbonization and energy efficiency goals in the smart energy grid. They assume the coordination of local energy resources such as energy generation, storage, and consumption. They are used to tackle problems brought by the stochastic nature of renewable energy, lack of energy storage devices, or insufficient local energy flexibility on the demand side. VPP modeling, management, and optimization are open to research problems that should consider, on one side, the local constraints in the operation of the energy resources and power flows and the energy grid’s sustainability objectives on the other side. There are multiple goals to create a VPP, such as to deliver energy services on a market or to the grid operator, to operate a microgrid in autonomy decoupled from the main grid, or to sustain local energy communities. In this paper, we present the results of a narrative review carried out on the domain of VPP optimization for the local energy grid integration. We have defined a search strategy that considers highly rated international databases (i.e., Elsevier, IEEE, and MDPI) in a six-year timeframe and applied objective inclusion/exclusion criteria for selecting articles and publications for the review; 95 articles have been analyzed and classified according to their objectives and solutions proposed for optimizing VPP integration in smart grids. The results of the study show that VPP concepts and applications are well addressed in the research literature, however, there is still work to be done on: engaging prosumers and citizens in such a virtual organization, developing heuristics to consider a wider range of local and global constraints and non-energy vectors, and to decentralize and make transparent the services delivery and financial settlement towards community members. This study can help researchers to understand the current directions for VPP integration in smart grids. As a next step we plan to further analyze the open research directions related to this problem and target the development of innovative solutions to allow the integration of multi-energy assets and management of cross energy sector services in energy communities.

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“…To test the optimization, a hybrid approach utilizing GWO [ 17 ] and PSO [ 18 ] was modeled for this purpose. In addition, this work involves using appliance energy prediction [ 19 ] to enable proactive energy optimization. The LSTM model was designed for energy prediction.…”

Section: Introductionmentioning

confidence: 99%

Energy Prediction and Optimization for Smart Homes with Weather Metric-Weight Coefficients

Mehmood

Lee

Kim

2023

Sensors

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Home appliances are considered to account for a large portion of smart homes’ energy consumption. This is due to the abundant use of IoT devices. Various home appliances, such as heaters, dishwashers, and vacuum cleaners, are used every day. It is thought that proper control of these home appliances can reduce significant amounts of energy use. For this purpose, optimization techniques focusing mainly on energy reduction are used. Current optimization techniques somewhat reduce energy use but overlook user convenience, which was the main goal of introducing home appliances. Therefore, there is a need for an optimization method that effectively addresses the trade-off between energy saving and user convenience. Current optimization techniques should include weather metrics other than temperature and humidity to effectively optimize the energy cost of controlling the desired indoor setting of a smart home for the user. This research work involves an optimization technique that addresses the trade-off between energy saving and user convenience, including the use of air pressure, dew point, and wind speed. To test the optimization, a hybrid approach utilizing GWO and PSO was modeled. This work involved enabling proactive energy optimization using appliance energy prediction. An LSTM model was designed to test the appliances’ energy predictions. Through predictions and optimized control, smart home appliances could be proactively and effectively controlled. First, we evaluated the RMSE score of the predictive model and found that the proposed model results in low RMSE values. Second, we conducted several simulations and found the proposed optimization results to provide energy cost savings used in appliance control to regulate the desired indoor setting of the smart home. Energy cost reduction goals using the optimization strategies were evaluated for seasonal and monthly patterns of data for result verification. Hence, the proposed work is considered a better candidate solution for proactively optimizing the energy of smart homes.

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Influencing Factors Evaluation of Machine Learning-Based Energy Consumption Prediction

Cited by 20 publications

References 36 publications

Machine Learning for Exploration of the Factors Affecting the Building Energy Usage: A Case Study

Machine Learning for Exploration of the Factors Affecting the Building Energy Usage: A Case Study

Virtual Power Plant Optimization in Smart Grids: A Narrative Review

Energy Prediction and Optimization for Smart Homes with Weather Metric-Weight Coefficients

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