A Novel Method for Hourly Electricity Demand Forecasting

Zhang, Guoqiang; Guo, Jifeng

doi:10.1109/tpwrs.2019.2941277

Cited by 58 publications

(18 citation statements)

References 43 publications

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“…Chin-Chia Hsu et al [105], at first proposed a GA-SVR model to overcome the problem of SVR parameters. SVR has been integrated with an improved adaptive genetic algorithm (IAGA) [106] to optimize the ratio values of meteorological factors and electricity cost, outperforming state-of-the-arts. A separate hybrid genetic-based SVR model (HGASVR) has been proposed by Wu et al [102].…”

Section: Svm and Genetic Algoritm (Svm-ga)mentioning

confidence: 99%

A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models

et al. 2020

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Load forecasting is a pivotal part of the power utility companies. To provide load-shedding free and uninterrupted power to the consumer, decision-makers in the utility sector must forecast the future demand for electricity with a minimum amount of error percentage. Load prediction with less percentage of error can save millions of dollars to the utility companies. There are numerous Machine Learning (ML) techniques to amicably forecast the demand of electricity among which the hybrid models show the best result. Two or more than two predictive models are amalgamated to design a hybrid model, each of which provides improved performances by the merit of individual algorithms. This paper reviews the current stateof-the-art of electric load forecasting technologies and presents recent works pertaining to the combination of different ML algorithms into two or more methods for the construction of hybrid models. A comprehensive study of each single and multiple load forecasting model is performed with an in-depth analysis of their advantages, disadvantages, and functions. A comparison between their performance in terms of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) values are developed with pertinent literature of several models to aid the researchers with the selection of suitable models for load prediction.

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Section: Svm and Genetic Algoritm (Svm-ga)mentioning

confidence: 99%

A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models

et al. 2020

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“…In very recent research, Zhang et al used SVR and adaptive GA to optimize the parameters to get the best load forecasting model [33]. They performed and validated their results on a specific ratio value using very small datasets.…”

Section: Related Workmentioning

confidence: 99%

Big Data Analytics for Energy Consumption Prediction in Smart Grid Using Genetic Algorithm and Long Short Term Memory

Kumari

Kumar

Rana

2021

cai

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Smart Grids (SG) have smart meters and advance metering infrasturutre (AMI) which generates huge data. This data can be used for predicting energy consumption using big data analytics. A very limited work has been carried out in the literature which shows the utilization of big data in energy consumption prediction. In this paper, the proposed method is based on Genetic Algorithm -Long Short Term Memory (GA-LSTM). LSTM memorises values over an arbitrary interval that manages time series data very effictively while GA is an evolutionary process that is used for optimization. GA combines with LSTM to process hyperparameters such as hidden layers, epochs, data intervals, batchsize and activation functions. Hence, GA creates a new vector for optimum solution that provides minimum error. These methods provide the best performance when compared with existing benchmarks. Moreover, GA-LSTM is used in a multi-threaded environment which increases the speed of convergence. Here, the multi-core platform is operated for solving one-dimensional GA-based inverse scattering problems. The result shows that GA-LSTM provides better convergence as compared to random approach techniques. For validating the results, Pennsylvania-New Jersey-Maryland Interconnection (PJM) energy consumption data has been used while adopting different performance evaluation metrics.

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“…The majority of existing publications have been focusing on short-term forecasting techniques (STF), and far fewer research has been done on long-term forecasting (LTF). Artificial neural networks have been applied in many STF load forecasting approaches, including the extreme learning machine approach [29,30], the support vector regression [31], the timevarying autoregressive model [32], as well as the semi-parametric additive model [33]. Statistical time series models such as autoregressive (AR), moving average (MA), and autoregressive integrated moving average (ARIMA) can also be applied to prediction, especially when the problems are modelled on dis-crete time series [34].…”

Section: Forecasting Of Indicator Future Valuesmentioning

confidence: 99%

Key index framework for quantitative sustainability assessment of energy infrastructures in a smart city: An example of Western Sydney

Yang

Liu

Wang

et al. 2020

Energy Conversion and Economics

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Human society is experiencing a rapidly changing environment in which energy shortages and an ongoing climate crisis have been identified as two of the major challenges to the sustainable development of human civilization. In the face of these challenges, the concept of a smart city is proposed which aims at achieving sustainable development, increasing the quality of life, and improving the cost‐effectiveness of existing and new energy infrastructures. To this end, this study proposes a general framework with a three‐tier story chart for guiding the establishment of sustainability assessment models and the selection of their indicators. In addition, a quantitative analysis method is developed for assessing the sustainability of energy infrastructures in a smart city, which indicates how the long‐term sustainability of the energy infrastructure can be achieved. The proposed method incorporates extensive environmental, economic, and social indicators, which go beyond conventional facility‐level criteria and seamlessly relate to the broader community that benefits from the renewable energy transition (including energy construction, operations, and energy services). The proposed methodologies can be implemented through collecting the corresponding history data of the indicators and following the analysis procedures presented in this study. The proposed methodology can serve as a supporting tool for decision‐making on new infrastructure investment and policymaking toward sustainable development. Case studies in Western Sydney were carried out to demonstrate the feasibility and efficiency of the proposed methodologies.

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A Novel Method for Hourly Electricity Demand Forecasting

Cited by 58 publications

References 43 publications

A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models

A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models

Big Data Analytics for Energy Consumption Prediction in Smart Grid Using Genetic Algorithm and Long Short Term Memory

Key index framework for quantitative sustainability assessment of energy infrastructures in a smart city: An example of Western Sydney

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