Time-Section Fusion Pattern Classification Based Day-Ahead Solar Irradiance Ensemble Forecasting Model Using Mutual Iterative Optimization

Accurate solar PV power forecasting can provide expected future PV output power so as to help the system operator to dispatch traditional power plants to maintain the balance between supply and demand sides. However, under non-stationary weather conditions, such as cloudy or partly cloudy days, the variability of solar irradiance makes the accurate PV power forecasting a very hard task. Ensemble forecasting based on multiple models established by different theory has been proved as an effective means on improving forecasting accuracy. Classification modeling according to different patterns could reduce the complexity and difficulty of intro-class data fitting so as to improve the forecasting accuracy as well. When combining the two above points and focusing on the different fusion pattern specifically in terms of hourly time dimension, a time-section fusion pattern classification based day-ahead solar irradiance ensemble forecasting model using mutual iterative optimization is proposed, which contains multiple forecasting models based on wavelet decomposition (WD), fusion pattern classification model, and fusion models corresponding to each fusion pattern. First, the solar irradiance is forecasted using WD based models at different WD level. Second, the fusion pattern classification recognition model is trained and then applied to recognize the different fusion pattern at each hourly time section. At last, the final forecasting result is obtained using the optimal fusion model corresponding to the data fusion pattern. In addition, a mutual iterative optimization framework for the pattern classification and data fusion models is also proposed to improve the model’s performance. Simulations show that the mutual iterative optimization framework can effectively enhance the performance and coordination of pattern classification and data fusion models. The accuracy of the proposed solar irradiance day-ahead ensemble forecasting model is verified when compared with a standard Artificial Neural Network (ANN) forecasting model, five WD based models and a single ensemble forecasting model without time-section fusion classification.

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Ultra-Short-Term Wind Power Forecasting Model Based on Time-Section Fusion and Pattern Classification

2020 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia) ◽

10.1109/icpsasia48933.2020.9208526 ◽

2020 ◽

Author(s):

Shuo Zhang ◽

Zhao Zhen ◽

Fei Wang ◽

Kangping Li ◽

Gang Qiu ◽

...

Keyword(s):

Wind Power ◽

Pattern Classification ◽

Forecasting Model ◽

Short Term ◽

Wind Power Forecasting ◽

Time Section ◽

Model Based ◽

Power Forecasting

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Wind Power Short-Term Forecasting Hybrid Model Based on CEEMD-SE Method

Processes ◽

10.3390/pr7110843 ◽

2019 ◽

Vol 7 (11) ◽

pp. 843 ◽

Cited By ~ 5

Author(s):

Keke Wang ◽

Dongxiao Niu ◽

Lijie Sun ◽

Hao Zhen ◽

Jian Liu ◽

...

Keyword(s):

Wind Power ◽

Extreme Learning Machine ◽

Harmony Search ◽

Forecasting Model ◽

Short Term ◽

Forecasting Accuracy ◽

Wind Power Forecasting ◽

Correlation Degree ◽

Learning Machine ◽

Power Forecasting

Accurately predicting wind power is crucial for the large-scale grid-connected of wind power and the increase of wind power absorption proportion. To improve the forecasting accuracy of wind power, a hybrid forecasting model using data preprocessing strategy and improved extreme learning machine with kernel (KELM) is proposed, which mainly includes the following stages. Firstly, the Pearson correlation coefficient is calculated to determine the correlation degree between multiple factors of wind power to reduce data redundancy. Then, the complementary ensemble empirical mode decomposition (CEEMD) method is adopted to decompose the wind power time series to decrease the non-stationarity, the sample entropy (SE) theory is used to classify and reconstruct the subsequences to reduce the complexity of computation. Finally, the KELM optimized by harmony search (HS) algorithm is utilized to forecast each subsequence, and after integration processing, the forecasting results are obtained. The CEEMD-SE-HS-KELM forecasting model constructed in this paper is used in the short-term wind power forecasting of a Chinese wind farm, and the RMSE and MAE are as 2.16 and 0.39 respectively, which is better than EMD-SE-HS-KELM, HS-KELM, KELM and extreme learning machine (ELM) model. According to the experimental results, the hybrid method has higher forecasting accuracy for short-term wind power forecasting.

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Wind Power Forecasting Model Fusion Evaluation Based on Comprehensive Weights

Volume 6B: Energy ◽

10.1115/imece2016-65668 ◽

2016 ◽

Cited By ~ 1

Author(s):

Jianyan Tian ◽

Tingting Liu ◽

Amit Banerjee ◽

Aixue Wei ◽

Shengqiang Yang ◽

...

Keyword(s):

Wind Power ◽

Evaluation Method ◽

Forecasting Model ◽

Model Optimization ◽

Forecasting Accuracy ◽

Wind Power Forecasting ◽

Single Index ◽

Forecasting Models ◽

The Individual ◽

Power Forecasting

Studies show that fusion modeling can improve the forecasting accuracy of wind power. Fusion modeling is the process of selective use of information from individual forecasting models. The reasonable evaluation of the individual models is the premise and basis of model optimization so that the individual models with high forecasting accuracy can be selected to establish the fusion model. Because the results of a single index model evaluation may not be comprehensive, the multi-index fusion evaluation method based on maximizing deviations and subjective correction is proposed. The method is applied to the selection of short-term wind power forecasting models. Firstly, this method establishes the individual model base of wind power forecasting model. Secondly, it establishes the more comprehensive evaluation index system. Thirdly, it combines maximizing deviations with the subjective correction coefficient to determine the comprehensive weight of each model, which is used to calculate the fusion evaluation value and get the evaluation order to achieve the model optimization. Finally, based on five years of data from a wind power plant in Shanxi Province, the validated experiments by multiple sets of forecasting data have been done using MATLAB in this paper. The simulation results demonstrate that the evaluation based on the proposed fusion evaluation method is more comprehensive and stable compared to evaluation using a single index. More importantly, it can effectively guide the model optimization with simple operating steps.

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Very Short-Term Photovoltaic Power Forecasting Using Stochastic Factors

ECTI Transactions on Computer and Information Technology (ECTI-CIT) ◽

10.37936/ecti-cit.2019132.198498 ◽

2020 ◽

Vol 13 (2) ◽

pp. 188-195

Author(s):

Kriangkamon Khumma ◽

Kreangsak Tamee

Keyword(s):

Forecasting Model ◽

Short Term ◽

Forecasting Accuracy ◽

Photovoltaic Power ◽

Minute Period ◽

Filtering Technique ◽

Stochastic Forecasting ◽

Short Term Forecasting ◽

Persistence Model ◽

Power Forecasting

This paper proposes a photovoltaic (PV) power forecasting model, using the application of a Gaussian blur algorithm ﬁltering technique to estimate power output and the creation of a stochastic forecasting model. As a result, aﬀected power can be forecasted from stochastic factors with machine learning and an artiﬁcial neural network. This model focuses on very short-term forecasting over a ﬁve minute period. As it uses only endogenous data, no exogenous data is needed. To evaluate the model, results were compared to the persistence model, which has good short-term forecasting accuracy. This proposed PV forecasting model gained higher accuracy than the persistence model using stochastic factors.

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A Novel Ensemble Approach for the Forecasting of Energy Demand Based on the Artificial Bee Colony Algorithm

Energies ◽

10.3390/en13030550 ◽

2020 ◽

Vol 13 (3) ◽

pp. 550 ◽

Cited By ~ 3

Author(s):

Jun Hao ◽

Xiaolei Sun ◽

Qianqian Feng

Keyword(s):

Energy Demand ◽

Artificial Bee Colony ◽

Hypothesis Test ◽

Ensemble Forecasting ◽

Forecasting Model ◽

Ensemble Model ◽

Forecasting Accuracy ◽

Abc Algorithm ◽

Ensemble Approach ◽

Bee Colony

Accurate forecasting of the energy demand is crucial for the rational formulation of energy policies for energy management. In this paper, a novel ensemble forecasting model based on the artificial bee colony (ABC) algorithm for the energy demand was proposed and adopted. The ensemble model forecasts were based on multiple time variables, such as the gross domestic product (GDP), industrial structure, energy structure, technological innovation, urbanization rate, population, consumer price index, and past energy demand. The model was trained and tested using the primary energy demand data collected in China. Seven base models, including the regression-based model and machine learning models, were utilized and compared to verify the superior performance of the ensemble forecasting model proposed herein. The results revealed that (1) the proposed ensemble model is significantly superior to the benchmark prediction models and the simple average ensemble prediction model just in terms of the forecasting accuracy and hypothesis test, (2) the proposed ensemble approach with the ABC algorithm can be employed as a promising framework for energy demand forecasting in terms of the forecasting accuracy and hypothesis test, and (3) the forecasting results obtained for the future energy demand by the ensemble model revealed that the future energy demand of China will maintain a steady growth trend.

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Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model

Open Physics ◽

10.1515/phys-2021-0043 ◽

2021 ◽

Vol 19 (1) ◽

pp. 360-374

Author(s):

Yuan Pei ◽

Lei Zhenglin ◽

Zeng Qinghui ◽

Wu Yixiao ◽

Lu Yanli ◽

...

Keyword(s):

Time Series ◽

Deep Learning ◽

Time Series Data ◽

Load Forecasting ◽

Series Data ◽

Forecasting Model ◽

Combined Model ◽

Forecasting Accuracy ◽

Forecasting Method ◽

Consumption Reduction

Abstract The load of the showcase is a nonlinear and unstable time series data, and the traditional forecasting method is not applicable. Deep learning algorithms are introduced to predict the load of the showcase. Based on the CEEMD–IPSO–LSTM combination algorithm, this paper builds a refrigerated display cabinet load forecasting model. Compared with the forecast results of other models, it finally proves that the CEEMD–IPSO–LSTM model has the highest load forecasting accuracy, and the model’s determination coefficient is 0.9105, which is obviously excellent. Compared with other models, the model constructed in this paper can predict the load of showcases, which can provide a reference for energy saving and consumption reduction of display cabinet.

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Short-term Photovoltaic Power Forecasting Model Based on Improved BP Neural Network Method

2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2) ◽

10.1109/ei250167.2020.9346675 ◽

2020 ◽

Author(s):

Yuan Peng ◽

Suhua Lou ◽

Senlin Yang ◽

Yaowu Wu ◽

Xianglu He ◽

...

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Forecasting Model ◽

Short Term ◽

Neural Network Method ◽

Model Based ◽

Photovoltaic Power ◽

Network Method ◽

Power Forecasting

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Notice of Retraction: An ensemble forecasting model for port throughput

2010 2nd International Conference on Industrial and Information Systems ◽

10.1109/indusis.2010.5565665 ◽

2010 ◽

Author(s):

Lili Qu ◽

Yan Chen ◽

Shengjun Qin ◽

Shuyong Liu

Keyword(s):

Ensemble Forecasting ◽

Forecasting Model ◽

Port Throughput

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An Ensemble Energy Consumption Forecasting Model Based on Spatial-Temporal Clustering Analysis in Residential Buildings

Energies ◽

10.3390/en14113020 ◽

2021 ◽

Vol 14 (11) ◽

pp. 3020

Author(s):

Anam-Nawaz Khan ◽

Naeem Iqbal ◽

Atif Rizwan ◽

Rashid Ahmad ◽

Do-Hyeun Kim

Keyword(s):

Cluster Analysis ◽

Deep Learning ◽

Residential Buildings ◽

Ensemble Forecasting ◽

Forecasting Model ◽

Ensemble Model ◽

Learning Models ◽

Short Term ◽

Floor Level ◽

Consumption Data

Due to the availability of smart metering infrastructure, high-resolution electric consumption data is readily available to study the dynamics of residential electric consumption at finely resolved spatial and temporal scales. Analyzing the electric consumption data enables the policymakers and building owners to understand consumer’s demand-consumption behaviors. Furthermore, analysis and accurate forecasting of electric consumption are substantial for consumer involvement in time-of-use tariffs, critical peak pricing, and consumer-specific demand response initiatives. Alongside its vast economic and sustainability implications, such as energy wastage and decarbonization of the energy sector, accurate consumption forecasting facilitates power system planning and stable grid operations. Energy consumption forecasting is an active research area; despite the abundance of devised models, electric consumption forecasting in residential buildings remains challenging due to high occupant energy use behavior variability. Hence the search for an appropriate model for accurate electric consumption forecasting is ever continuing. To this aim, this paper presents a spatial and temporal ensemble forecasting model for short-term electric consumption forecasting. The proposed work involves exploring electric consumption profiles at the apartment level through cluster analysis based on the k-means algorithm. The ensemble forecasting model consists of two deep learning models; Long Short-Term Memory Unit (LSTM) and Gated Recurrent Unit (GRU). First, the apartment-level historical electric consumption data is clustered. Later the clusters are aggregated based on consumption profiles of consumers. At the building and floor level, the ensemble models are trained using aggregated electric consumption data. The proposed ensemble model forecasts the electric consumption at three spatial scales apartment, building, and floor level for hourly, daily, and weekly forecasting horizon. Furthermore, the impact of spatial-temporal granularity and cluster analysis on the prediction accuracy is analyzed. The dataset used in this study comprises high-resolution electric consumption data acquired through smart meters recorded on an hourly basis over the period of one year. The consumption data belongs to four multifamily residential buildings situated in an urban area of South Korea. To prove the effectiveness of our proposed forecasting model, we compared our model with widely known machine learning models and deep learning variants. The results achieved by our proposed ensemble scheme verify that model has learned the sequential behavior of electric consumption by producing superior performance with the lowest MAPE of 4.182 and 4.54 at building and floor level prediction, respectively. The experimental findings suggest that the model has efficiently captured the dynamic electric consumption characteristics to exploit ensemble model diversities and achieved lower forecasting error. The proposed ensemble forecasting scheme is well suited for predictive modeling and short-term load forecasting.

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