A short-term residential load forecasting scheme based on the multiple correlation-temporal graph neural networks

Wang, Yufeng; Rui, Liming; Ma, Jun; Jin, Qun

doi:10.1016/j.asoc.2023.110629

Cited by 12 publications

(4 citation statements)

References 24 publications

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“…The data available on the LASS website covers the period between 2014 and 2016. The dataset from LASS has been widely used and validated in various research studies [58]. The datasets were sourced from five distinct homes situated across Massachusetts, USA, each equipped with multiple appliances and devices.…”

Section: Problem Statement and Datasetsmentioning

confidence: 99%

An Intelligent Hybrid Machine Learning Model for Sustainable Forecasting of Home Energy Demand and Electricity Price

Parizad,

Ranjbarzadeh,

Jamali

et al. 2024

Sustainability

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Home energy systems (HESs) face challenges, including high energy costs, peak load impact, and reliability issues associated with grid connections. To address these challenges, homeowners can implement solutions such as energy management, renewable resources, and energy storage technologies. Understanding consumption patterns and optimizing HES operations are crucial for effective energy management. As a primary step, addressing these concerns requires an efficient forecasting tool to predict home energy demand and electricity prices. Due to the complexity of big data, and uncertainties involved in forecasting, machine learning (ML) methods are necessary. In this study, we develop a hybrid machine learning approach, utilizing one year of data on home energy demand and prices to address the challenge of forecasting home energy consumption. A comprehensive comparison of different deep and non-deep ML models highlights the superiority of the proposed hybrid approach. The performance of these models, measured using metrics such as RMSE, MAE, R2, and RT (running time), are compared. Finally, an optimized hybrid XGBoost (XGB) ML model that combines price and energy demand forecasting is introduced. The proposed ML method’s parameters are optimally determined using Particle Swarm Optimization. The hybrid ML model’s performance is evaluated in predicting both energy demand and consumption prices using historical data from diverse households with various features and consumption patterns. The results indicate that the hybrid ML model achieves accurate predictions for energy consumption and prices, with improvements in RMSE (up to 36.6%), MAE (up to 36.8%), and R2 (up to 3.9), as compared to conventional ML methods. This research contributes to sustainable energy practices by providing an effective tool for forecasting energy consumption and associated costs in the dynamic landscape of home energy systems.

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Section: Problem Statement and Datasetsmentioning

confidence: 99%

An Intelligent Hybrid Machine Learning Model for Sustainable Forecasting of Home Energy Demand and Electricity Price

Parizad,

Ranjbarzadeh,

Jamali

et al. 2024

Sustainability

View full text Add to dashboard Cite

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“…Compared with other seasons, the load curve in summer has the characteristics of short peak periods and large differences between peaks and valleys [47], resulting in low WPVP consumption in summer. As a result, the season of the example is set to summer to analyze the effect of this paper's DR strategy on the consumption of WPVP.…”

Section: Analysis Of Wpvp Consumptionmentioning

confidence: 99%

Wind and PV Power Consumption Strategy Based on Demand Response: A Model for Assessing User Response Potential Considering Differentiated Incentives

Zhao,

Wu,

Zhou

et al. 2024

Sustainability

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In China, the inversion between peak periods of wind and photovoltaic (PV) power (WPVP) generation and peak periods of electricity demand leads to a mismatch between electricity demand and supply, resulting in a significant loss of WPVP. In this context, this article proposes an improved demand response (DR) strategy to enhance the consumption of WPVP. Firstly, we use feature selection methods to screen variables related to response quantity and, based on the results, establish a response potential prediction model using random forest algorithm. Then, we design a subsidy price update formula and the subsidy price constraint conditions that consider user response characteristics and predict the response potential of users under differentiated subsidy price. Subsequently, after multiple iterations of the price update formula, the final subsidy and response potential of the user can be determined. Finally, we establish a user ranking sequence based on response potential. The case analysis shows that differentiated price strategy and response potential prediction model can address the shortcomings of existing DR strategies, enabling users to declare response quantity more reasonably and the grid to formulate subsidy price more fairly. Through an improved DR strategy, the consumption rate of WPVP has increased by 12%.

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“…Accurate load forecasting is essential for power systems to satisfy the increasing electricity demand. It enables effective planning of generation and distribution, ensuring the long-term sustainability of the power supply [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Short‐term electric load forecasting based on empirical wavelet transform and temporal convolutional network

Zhao,

Lin

2024

IET Generation Trans & Dist

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Short‐term load forecasting is the basis of power system operation and analysis and is of great significance for the stable operation of power systems. To solve the problems of insufficient mining of the intrinsic information of load data, randomness, and non‐linearity, and to improve the accuracy of load prediction, the authors propose a short‐term power load prediction model based on empirical wavelet transform (EWT) decomposition and the temporal convolutional network (TCN). First, the Pearson coefficient is used to eliminate the less relevant influencing factors to reduce the complexity of the model. Then, the EWT algorithm is used to decompose the original load signal to fully exploit the load data time domain and frequency domain information, while solving the problems of non‐linearity and randomness. Finally, the decomposed subsequences are input to the TCN network for prediction superposition to obtain the final results. The experimental results show that, compared with the single models TCN, gated recurrent units (GRU), and long short‐term memory (LSTM), and the hybrid models EWT‐GRU, EWT‐LSTM, and VMD‐TCN, the R2 of the short‐term power load forecasting model based on EWT‐TCN is improved by 0.2099%, 0.2519%, 0.3453%, 0.0334%, 0.1766%, and 0.1228%, respectively.

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A short-term residential load forecasting scheme based on the multiple correlation-temporal graph neural networks

Cited by 12 publications

References 24 publications

An Intelligent Hybrid Machine Learning Model for Sustainable Forecasting of Home Energy Demand and Electricity Price

An Intelligent Hybrid Machine Learning Model for Sustainable Forecasting of Home Energy Demand and Electricity Price

Wind and PV Power Consumption Strategy Based on Demand Response: A Model for Assessing User Response Potential Considering Differentiated Incentives

Short‐term electric load forecasting based on empirical wavelet transform and temporal convolutional network

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