A combined model based on data pre-analysis and weight coefficients optimization for electrical load forecasting

Xiao, Liye; Wang, Jianzhou; Hou, Ru; Wu, Jie

doi:10.1016/j.energy.2015.01.063

Cited by 129 publications

(46 citation statements)

References 63 publications

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“…In addition, information gain, which is meant to be maximized in the decision processes, has the lowest value as zero (0) and the highest value as one (1). In some other texts, this is called gain ratio, which draws many of relationships from the entropy.…”

Section: Decision Tree (Dt)mentioning

confidence: 99%

“…However, cooperative short-term load techniques, which involve collaboration of more than one model, have proven to be more efficient and accurate [10]. In this regard, cooperative models can drastically reduce the large forecasting errors inherent in the classical techniques [1].…”

Section: Introductionmentioning

confidence: 99%

“…Predicting electricity demand is crucial, since it plays a significant role in the administration, decision-making and demand planning of utility power supply operations [1]. Effectiveness and accuracy in terms of extremely reduced forecasting error of a predictive model cannot be overemphasised, as load forecasting guides power grid operations and power station construction planning.…”

Section: Introductionmentioning

confidence: 99%

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Short-Term Multiple Forecasting of Electric Energy Loads for Sustainable Demand Planning in Smart Grids for Smart Homes

Alani

Osunmakinde

2017

Sustainability

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Abstract:Energy consumption in the form of fuel or electricity is ubiquitous globally. Among energy types, electricity is crucial to human life in terms of cooking, warming and cooling of shelters, powering of electronic devices as well as commercial and industrial operations. Users of electronic devices sometimes consume fluctuating amounts of electricity generated from smart-grid infrastructure owned by the government or private investors. However, frequent imbalance is noticed between the demand and supply of electricity, hence effective planning is required to facilitate its distribution among consumers. Such effective planning is stimulated by the need to predict future consumption within a short period. Although several interesting classical techniques have been used for such predictions, they still require improvement for the purpose of reducing significant predictive errors when used for short-term load forecasting. This research develops a near-zero cooperative probabilistic scenario analysis and decision tree (PSA-DT) model to address the lacuna of enormous predictive error faced by the state-of-the-art models. The PSA-DT is based on a probabilistic technique in view of the uncertain nature of electricity consumption, complemented by a DT to reinforce the collaboration of the two techniques. Based on detailed experimental analytics on residential, commercial and industrial data loads, the PSA-DT model outperforms the state-of-the-art models in terms of accuracy to a near-zero error rate. This implies that its deployment for electricity demand planning will be of great benefit to various smart-grid operators and homes.

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Section: Decision Tree (Dt)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Short-Term Multiple Forecasting of Electric Energy Loads for Sustainable Demand Planning in Smart Grids for Smart Homes

Alani

Osunmakinde

2017

Sustainability

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“…Electric load forecasting plays an essential role in making optimal action plans for decision makers, such as load unit commitment, energy transfer scheduling, contingency planning load shedding, energy generation, load dispatch, power system operation security, hydrothermal coordination, and so on [1]. Indicated by Bunn and Farmer [2], an 1% increase in electric load forecasting error may lead to a £10 million additional expenditure in operations.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Chaotic Quantum Bat Algorithm with SVR in Electric Load Forecasting

Geng

Wang

et al. 2017

Energies

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Abstract:Hybridizing evolutionary algorithms with a support vector regression (SVR) model to conduct the electric load forecasting has demonstrated the superiorities in forecasting accuracy improvements. The recently proposed bat algorithm (BA), compared with classical GA and PSO algorithm, has greater potential in forecasting accuracy improvements. However, the original BA still suffers from the embedded drawbacks, including trapping in local optima and premature convergence. Hence, to continue exploring possible improvements of the original BA and to receive more appropriate parameters of an SVR model, this paper applies quantum computing mechanism to empower each bat to possess quantum behavior, then, employs the chaotic mapping function to execute the global chaotic disturbance process, to enlarge bat's search space and to make the bat jump out from the local optima when population is over accumulation. This paper presents a novel load forecasting approach, namely SVRCQBA model, by hybridizing the SVR model with the quantum computing mechanism, chaotic mapping function, and BA, to receive higher forecasting accuracy. The numerical results demonstrate that the proposed SVRCQBA model is superior to other alternative models in terms of forecasting accuracy.

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“…A series of researches which solved the problem have been made in recent years. A combined model based on data pre-analysis was proposed for electrical load forecasting and cuckoo search algorithm was applied to optimize the weight coefficients [25]. A combined model has been developed for electric load forecasting and adaptive particle swarm optimization (APSO) algorithm was used to determine the weight coefficients allocated to each individual model [26].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model Equipped with the Minimum Cycle Decomposition Concept for Short-Term Forecasting of Electrical Load Time Series

Shen³

et al. 2017

Neural Process Lett

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Electricity load forecasting is an essential, however complicated work. Due to the influence of a large number of uncertain factors, it shows complicated nonlinear combination features. Therefore, it is difficult to improve the prediction accuracy and the tremendous breadth of applicability especially for using a single method. In order to improve the performance including accuracy and applicability of electricity load forecasting, in this paper, a concept named minimum cycle decomposition (MCD) that the raw data are grouped according to the minimum cycle was proposed for the first time. In addition, a hybrid prediction model (HMM) based on one-order difference, ensemble empirical model decomposition (EEMD), mind evolutionary computation (MEC) and wavelet neural network (WNN) was also proposed in this study. The HMM model consists of two parts. Part one, pre-processing, known as one order difference to remove the trend of subsequence and EEMD to reduce the noise, was performed by HMM model on each subset. Part two, the WNN optimized by MEC (WNN + MEC) was applied on resultant subseries. Finally, a number of different models were used as the comparative experiment to validate the effectiveness of the presented method, such as back propagation neural network (BP-1), BPNN combined MCD (BP-2), WNN combined MCD (WNNM), a HMM (DEEPLSSVM) based on one-order difference, EEMD, particle swarm optimization and least squares support vector machine and a hybrid model (DEESGRNN) based on one-order difference, EEMD, simulate anneal and generalized regression neural network. Certain evaluation measurements are taken into account to assess the performance. Experiments were carried out on QLD (Queensland) and NSW (New South Wales) electricity markets historical data, and the experimental results show that the MCD has the advantages of improving model accuracy and of generalization ability. In addition, the simulation results also suggested that the proposed hybrid model has better performance. B Zhaoshuang He

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A combined model based on data pre-analysis and weight coefficients optimization for electrical load forecasting

Cited by 129 publications

References 63 publications

Short-Term Multiple Forecasting of Electric Energy Loads for Sustainable Demand Planning in Smart Grids for Smart Homes

Short-Term Multiple Forecasting of Electric Energy Loads for Sustainable Demand Planning in Smart Grids for Smart Homes

Hybrid Chaotic Quantum Bat Algorithm with SVR in Electric Load Forecasting

A Hybrid Model Equipped with the Minimum Cycle Decomposition Concept for Short-Term Forecasting of Electrical Load Time Series

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