A novel ensemble method for hourly residential electricity consumption forecasting by imaging time series

Zhang, Guoqiang; Guo, Jifeng

doi:10.1016/j.energy.2020.117858

Cited by 39 publications

(13 citation statements)

References 58 publications

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“…G. Zhang et al suggested a hybrid-ensemble approach to establish an estimate for power consumption and compared the accuracy of the results with other approaches. The proposed method has shown good performance in estimating hourly power consumption [210]. To estimate building energy demand across the city, a model was established using the 'SVR-QPSO' approach.…”

Section: Hybrid Methodsmentioning

confidence: 99%

A review on short‐term load forecasting models for micro‐grid application

Kondaiah

Saravanan

Sanjeevikumar

et al. 2022

The Journal of Engineering

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Load forecasting (LF), particularly short‐term load forecasting (STLF), plays a vital role throughout the operation of the conventional power system. The precise modelling and complex analyses of STLF have become more significant in advanced microgrid (MG) applications. Several models are proposed for STLF and tested successfully in the literature. The selection of a forecasting method is mostly based on data availability and its objectives. This article presents a survey of the latest analytical and approximation techniques reported in the literature to model STLF in an MG environment. This article mainly focusses on the review on important methods applied to forecast renewable energy availability, energy demand, and price and load demand. Different models, their main objectives, methodology, error percentage, and so forth, are critically reviewed and analysed. For quick reference, we have highlighted the important points in the form tables. The researchers can quickly identify and frame their research problem related to the LF area by reading this review paper.

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Section: Hybrid Methodsmentioning

confidence: 99%

A review on short‐term load forecasting models for micro‐grid application

Kondaiah

Saravanan

Sanjeevikumar

et al. 2022

The Journal of Engineering

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“…Traditional electricity consumption forecasting methods contain regression analysis [10], Markov models [11], support vector regression [12], time-series analysis models [13,14]. With the development of artificial intelligence technologies, machine learning models such as multi-birth support vector regression machines and neural networks [15,16] are applied to this task.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Scale Electricity Consumption Prediction Algorithm Based on Time-Frequency Variational Autoencoder

Zheng

Zhou

et al. 2021

IEEE Access

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Accurate electricity consumption forecasting can be treated as a reliable guidance for power production. However, traditional electricity forecasting models suffer from simultaneously capturing the periodicity and the volatility of sequential electricity consumption data, while the periodicity and the volatility are important for electricity forecasting. In order to effectively model this sequential data and predict electricity consumption accurately, we propose a multi-scale prediction (Long Short Term Memory, LSTM) algorithm based on Time-Frequency Variational Autoencoder (TFVAE-LSTM). The proposed algorithm treats the sequential data as a superposition of data in different frequencies, it defines an encoder in frequency domain to extract frequency features to model the periodicity and volatility, and defines a decoder in time domain to capture the sequential features of data. Based on the extracted Time-Frequency features in a TFVAE, a multi-scale LSTM model is defined to further extract sequential features from different scales to predict electricity consumption. Experiments show the effectiveness of the proposed TFVAE-LSTM for electricity consumption forecasting tasks.

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“…In addition, in several works [ 25 , 26 ] ensemble-based techniques have also been utilized together with sentiment analysis for time series forecasting in order to exploit the benefits of ensemble theory. In [ 27 ], an ensemble method, formed by combining LSTMs and ARIMA models under a feedforward neural network scheme, was proposed in order to predict future values of stock prices, utilizing sentiment analysis on data provided by scraping news related to the stock from the Internet.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Method Survey on the Use of Sentiment Analysis in Multivariate Financial Time Series Forecasting

Liapis

Karanikola

Kotsiantis

2021

Entropy

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In practice, time series forecasting involves the creation of models that generalize data from past values and produce future predictions. Moreover, regarding financial time series forecasting, it can be assumed that the procedure involves phenomena partly shaped by the social environment. Thus, the present work is concerned with the study of the use of sentiment analysis methods in data extracted from social networks and their utilization in multivariate prediction architectures that involve financial data. Through an extensive experimental process, 22 different input setups using such extracted information were tested, over a total of 16 different datasets, under the schemes of 27 different algorithms. The comparisons were structured under two case studies. The first concerns possible improvements in the performance of the forecasts in light of the use of sentiment analysis systems in time series forecasting. The second, having as a framework all the possible versions of the above configuration, concerns the selection of the methods that perform best. The results, as presented by various illustrations, indicate, on the one hand, the conditional improvement of predictability after the use of specific sentiment setups in long-term forecasts and, on the other, a universal predominance of long short-term memory architectures.

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A novel ensemble method for hourly residential electricity consumption forecasting by imaging time series

Cited by 39 publications

References 58 publications

A review on short‐term load forecasting models for micro‐grid application

A review on short‐term load forecasting models for micro‐grid application

A Multi-Scale Electricity Consumption Prediction Algorithm Based on Time-Frequency Variational Autoencoder

A Multi-Method Survey on the Use of Sentiment Analysis in Multivariate Financial Time Series Forecasting

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