Multivariate empirical mode decomposition based hybrid model for day-ahead peak load forecasting

Huang, Yanmei; Hasan, Najmul; Deng, Chenglong; Bao, Yukun

doi:10.1016/j.energy.2021.122245

Cited by 68 publications

(11 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Literature on load and peak forecasting can be clustered according to time, into short, medium and long term [34]. Another distinction can be made in the following categories: a) the conditional modeling approach, generally based on macroeconomic variables like inflation, GDP, Forex, etc.. [8][9][10][11][12], b) the system indicators of the electrical distribution and transmission system, such as the number of connections, machinery capacity etc., [13][14][15][16][17][18], c) the historical modeling approach [9,19] and d) hybrid models [20,21]. Finally, literature can be clustered around the method used, a distinction used by Weron [22], in the disciplines of a) time series analysis-statistics [8,21,23,24], b) informatics or computational intelligence and c) hybrid models [25][26][27][28].…”

Section: Literature Reviewmentioning

confidence: 99%

Electricity Demand, Forecasting the Peaks: Development and Implementation of C-EVA Method

Theodorou,

Christopoulos

2024

GLCE

View full text Add to dashboard Cite

Price spikes in electricity markets are very frequent, posing tremendous burden on household income and on manufacturing cost. Electricity demand (load) can be divided in two parts, energy (MWh) and peak (MW) and most of time peak is responsible for the price spikes. Literature review while devoting most of the discussion to energy, lags in the investigation of peak. In this research, a model for peak demand analysis and forecasting is developed. The model is based on a portfolio of cluster and extreme value analysis (C-EVA) methods using UIK, EDE and WSL innovations for the optimal determination of clusters and the daily peaks divulgence. C-EVA method consists of the Clustering part for optimal number of clusters determination and classification of day and month of peak, and the part of Extreme Value Analysis for computation of the statistical confidence interval for the load maxima. C-EVA after using all the currently available load maxima, estimates statistically the expected worst-case scenario for peaks of loads. Load peaks will be determined by EVA based on an estimated bimodal distribution while a signaling method will prompt the probability of extremes. The added value of the proposed method is that does not reject the extreme values as most methodologies do. Extreme Value Analysis for maxima and minima provide estimators for highest and lowest expected hourly load, while giving the confidence interval of the return level using an optimization method for the selection of a rolling time window, as the return period. It was found that distributed generation of renewables create a camel effect on the load peaks which increases sharpness. The proposed methodology solved this issue while opening the ground for future research for the role of storage, batteries as well as for virtual power plants as an integrated portfolio of renewables generation.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Electricity Demand, Forecasting the Peaks: Development and Implementation of C-EVA Method

Theodorou,

Christopoulos

2024

GLCE

View full text Add to dashboard Cite

show abstract

“…It starts with the random initialization of a population of particles, i.e., swarms in the search space, and then finds the global optimum by adjusting the path of particles towards two directions in each iteration: the individual best location of the individual particle, and the location of the best particle in the whole population. Compared to other optimization algorithms PSO has the ability to quickly converge to a reasonable solution at a lower computational cost and is therefore used in many applications including forecasting studies (Dong et al, 2015;Ghimire et al, 2019;Kuranga & Pillay, 2022;Huang et al, 2022).…”

Section: Pso-based Forecast Combination Approachmentioning

confidence: 99%

Multi-resolution, multi-horizon distributed solar PV power forecasting with forecast combinations

Perera¹,

Hoog²,

Bandara³

et al. 2022

Expert Systems with Applications

View full text Add to dashboard Cite

“…e flow rate is a weak node; then an iterative algorithm based on deviation correction is proposed for the weak node of natural gas flow; the equivalent parameters are tracked in real time; and the indicators of static natural gas flow analysis are obtained. After initial dimensionality reduction of feature attributes using scoring criteria, Huang's et al [16] improvements were made to the existing principal component analysis methods; the attributes after dimensionality reduction were classified; principal components were extracted; and the various principal components were combined as it.…”

Section: Related Workmentioning

confidence: 99%

A Forecast Model of City Natural Gas Daily Load Based on Data Mining

Chen

Zhang

2022

Scientific Programming

View full text Add to dashboard Cite

Data mining technology is more and more widely used in the daily load forecasting of natural gas systems. It is still difficult to carry out high-precision, timely intraday load forecasting and intraday load dynamic characteristics clustering for natural gas systems. Based on data mining technology, this paper proposes a stable intraday load forecasting method for the natural gas flow state-space model. The load sensitivity under the current operating conditions of the system is obtained by calculation; the sample space of the state space is established through data processing; the partitions under different clustering radii are calculated; and the best intraday load flow is obtained through the state space effectiveness evaluation method. The experimental results show that the model load forecasting accuracy and relative error reached 98.5% and 0.026, respectively, which solved the problem of processing the long-term accumulated historical data of gas intra-day load. At the same time, the amount of data calculation was reduced by 33.6%, which effectively promoted the quantification of intraday load influencing factors and qualitative analysis.

show abstract

Multivariate empirical mode decomposition based hybrid model for day-ahead peak load forecasting

Cited by 68 publications

References 62 publications

Electricity Demand, Forecasting the Peaks: Development and Implementation of C-EVA Method

Electricity Demand, Forecasting the Peaks: Development and Implementation of C-EVA Method

Multi-resolution, multi-horizon distributed solar PV power forecasting with forecast combinations

A Forecast Model of City Natural Gas Daily Load Based on Data Mining

Contact Info

Product

Resources

About