Day-ahead load forecast using random forest and expert input selection

Lahouar, Ali; Slama, Jaleleddine Ben Hadj

doi:10.1016/j.enconman.2015.07.041

Cited by 274 publications

(144 citation statements)

References 56 publications

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“…The electrical load forecasting has become more and more important in recent years due to the electricity market deregulation and integration of renewable resources. A number of forecasting methods have been developed in the literature based on neural networks [24], ensemble methods combining together multiple learning algorithms [19], etc.…”

Section: Expected Deviations From the Forecast Consumption Of Base Loadsmentioning

confidence: 99%

Designing coalition-based fair and stable pricing mechanisms under private information on consumers’ reservation prices

Cadre¹,

Pagnoncelli

Homem-de-Mello

et al. 2019

European Journal of Operational Research

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We model the relation between an aggregator and consumers joining a coalition to reduce the risk resulting from the unpredictability of their base load demand, as a Stackelberg game formulated as a mathematical bilevel program with private information on the consumers' reservation prices. At the upper-level of the Stackelberg game, the aggregator optimizes his daily price profile so as to reach a net targeted profit which is the maximum value guaranteeing that no consumer will leave the coalition -to contract with a conventional retailer considered here as a fixed alternative -while meeting fairness criterion imposed by the cost-sharing mechanism. At the lower-level, the consumers are asked to provide in day ahead an estimate of their base load hourly demand profile and to schedule their shiftable loads depending on the price signal sent by the aggregator. We provide algorithms that determine the unique price profile and consumer shiftable load schedules as functions of the reservation price estimates. The Stackelberg game between the aggregator and the consumers being repeated for a period of time, the aggregator has the possibility to update his estimates of the reservation prices relying on a feedback function which depends on the percentage of activated loads. A randomized algorithm for consumers' reservation price learning based on regret minimization is provided. For four cost-sharing mechanisms such as uniform allocation, stand-alone cost, Shapley value, separable and non-separable costs, we determine the closed form of the aggregator's optimal net targeted profit guaranteeing the stability of the coalition. We also determine conditions guaranteeing the core non-emptiness and prove that for a profit-maximizing aggregator, the stand-alone cost is always preferable to the Shapley value, which coincides with the uniform allocation. Furthermore, the optimal size of the coalition -in terms of the aggregator's profit -can be determined analytically when the Shapley value is implemented as cost-sharing mechanism. The results are illustrated on a case study where we show that there exists an optimal net targeted profit below which the consumers energy bill is lower when joining the aggregator than with the conventional retailer. Coalition dynamics is also analyzed numerically depending on the consumer inertia in their energy supplier choice process, for each cost-sharing mechanism.

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Section: Expected Deviations From the Forecast Consumption Of Base Loadsmentioning

confidence: 99%

Designing coalition-based fair and stable pricing mechanisms under private information on consumers’ reservation prices

Cadre¹,

Pagnoncelli

Homem-de-Mello

et al. 2019

European Journal of Operational Research

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“…After several trials of cross validation between different years, we find the default parameters of R package is generally ok for SVM. The main parameter of RFR are the number of trees ntree and the number of variables to partition at each tree node mtry, which do not have remarkable impact on the resulting accuracy according to the investigation of previous papers [6]. The tree number we have chosen for RFR is 1000 and variable number is 10 which are good enough to get satisfactory result.…”

Section: Training Algorithm and Processmentioning

confidence: 99%

“…For the purpose of electrical load forecast, we have used Random Forest and Support Vector Machine (SVM) which are popular methods for load forecast in recent years [4]- [6]. In 2001, EUNITE network organized a world wide competition on the daily electrical load prediction problem.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Time Warping and FFT: A Data Preprocessing Method for Electrical Load Forecasting

Huo¹

2018

ijacsa

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Abstract-For power suppliers, an important task is to accurately predict the short-term load. Thus many papers have introduced different kinds of artificial intelligent models to improve the prediction accuracy. In recent years, Random Forest Regression (RFR) and Support Vector Machine (SVM) are widely used for this purpose. However, they can not perform well when the sample data set is too noisy or with too few pattern feature. It is usually difficult to tell whether a regression algorithm can accurately predict the future load from the historical data set before trials. Here we demonstrate a method which estimates the similarity between time series by Dynamic Time Warping (DTW) combined with Fast Fourier Transform (FFT). Results show this is a simple and fast method to filter the raw large electrical load data set and improve the learning result before looping through all learning processes.

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“…Since the 1980s, to improve the accuracy of load forecasting, many artificial intelligent (AI) approaches have been used and been combined to develop powerful forecasting methods, such as artificial neural networks (ANNs) [17][18][19][20][21], expert system-based methods [22][23][24], and fuzzy inference…”

Section: Introductionmentioning

confidence: 99%

Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting

Lee

Lin

2016

Energies

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Abstract:Hybridizing chaotic evolutionary algorithms with support vector regression (SVR) to improve forecasting accuracy is a hot topic in electricity load forecasting. Trapping at local optima and premature convergence are critical shortcomings of the tabu search (TS) algorithm. This paper investigates potential improvements of the TS algorithm by applying quantum computing mechanics to enhance the search information sharing mechanism (tabu memory) to improve the forecasting accuracy. This article presents an SVR-based load forecasting model that integrates quantum behaviors and the TS algorithm with the support vector regression model (namely SVRQTS) to obtain a more satisfactory forecasting accuracy. Numerical examples demonstrate that the proposed model outperforms the alternatives.

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Day-ahead load forecast using random forest and expert input selection

Cited by 274 publications

References 56 publications

Designing coalition-based fair and stable pricing mechanisms under private information on consumers’ reservation prices

Designing coalition-based fair and stable pricing mechanisms under private information on consumers’ reservation prices

Dynamic Time Warping and FFT: A Data Preprocessing Method for Electrical Load Forecasting

Application of Hybrid Quantum Tabu Search with Support Vector Regression (SVR) for Load Forecasting

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