Daily pattern prediction based classification modeling approach for day-ahead electricity price forecasting

Wang, Fei; Li, Kangping; Zhou, Lidong; Ren, Hui; Contreras, Javier; Shafie‐khah, Miadreza; Catalão, João P.S.

doi:10.1016/j.ijepes.2018.08.039

Cited by 110 publications

(22 citation statements)

References 43 publications

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“…Artificial intelligence methods are ANN, Particle Swarm Optimization (PSO), etc. Classifier-based approaches are widely used for forecasting, such as SWA (Sperm Whale Algorithm) + LSSVM (Least Square Support Vector Machine) [13], SVM + PSO [14][15][16], empirical mode decomposition + Support Vector Regressor (SVR) [17], FWPT (Flexible Wavelet Packet Transform), TVABC (Time-Varying Artificial Bee Colony), LSSVM (FWPT + LSSVM + TVABC) [18], LSSVR + fruit fly algorithm [19], phase space reconstruction + bi-square kernel regression [20] and DE (Differential Evaluation) + SVM [21]. Although the aforementioned methods show reasonable results in load or price forecasting, they are computationally complex.…”

Section: Related Workmentioning

confidence: 99%

“…A forecasting method that can accurately forecast both load and price together is greatly required. Conventional forecasting methods in the literature have to extract most relevant features with great effort [13,14,18,21] Recently, Deep Neural Networks (DNNs) have shown promising results in forecasting of electricity load [25][26][27][28][29][30] and price [31][32][33]. In [25], the authors used Restricted Boltzman Machine (RBM) with pre-training and Rectified Linear Unit (ReLU) to forecast day and week ahead load.…”

Section: Related Workmentioning

confidence: 99%

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Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities

et al. 2019

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This paper focuses on analytics of an extremely large dataset of smart grid electricity price and load, which is difficult to process with conventional computational models. These data are known as energy big data. The analysis of big data divulges the deeper insights that help experts in the improvement of smart grid’s (SG) operations. Processing and extracting of meaningful information from data is a challenging task. Electricity load and price are the most influential factors in the electricity market. For improving reliability, control and management of electricity market operations, an exact estimate of the day ahead load is a substantial requirement. Energy market trade is based on price. Accurate price forecast enables energy market participants to make effective and most profitable bidding strategies. This paper proposes a deep learning-based model for the forecast of price and demand for big data using Deep Long Short-Term Memory (DLSTM). Due to the adaptive and automatic feature learning mechanism of Deep Neural Network (DNN), the processing of big data is easier with LSTM as compared to the purely data-driven methods. The proposed model was evaluated using well-known real electricity markets’ data. In this study, day and week ahead forecasting experiments were conducted for all months. Forecast performance was assessed using Mean Absolute Error (MAE) and Normalized Root Mean Square Error (NRMSE). The proposed Deep LSTM (DLSTM) method was compared to traditional Artificial Neural Network (ANN) time series forecasting methods, i.e., Nonlinear Autoregressive network with Exogenous variables (NARX) and Extreme Learning Machine (ELM). DLSTM outperformed the compared forecasting methods in terms of accuracy. Experimental results prove the efficiency of the proposed method for electricity price and load forecasting.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities

et al. 2019

View full text Add to dashboard Cite

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“…Plentiful methods have been developed for electricity price forecasting such as: statistical methods, computational intelligence methods and, nowadays with a greater propensity, hybrid methods [38][39][40][41]. These last further explore the best features of each individual method.…”

Section: Short-term Price Forecastingmentioning

confidence: 99%

Daily Operation Optimization of a Hybrid Energy System Considering a Short-Term Electricity Price Forecast Scheme

et al. 2019

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The scenario where the renewable generation penetration is steadily on the rise in an increasingly atomized system, with much of the installed capacity “sitting” on a distribution level, is in clear contrast with the “old paradigm” of a natural oligopoly formed by vertical structures. Thereby, the fading of the classical producer–consumer division to a broader prosumer “concept” is fostered. This crucial transition will tackle environmental harms associated with conventional energy sources, especially in this age where a greater concern regarding sustainability and environmental protection exists. The “smoothness” of this transition from a reliable conventional generation mix to a more volatile and “parti-colored" one will be particularly challenging, given escalating electricity demands arising from transportation electrification and proliferation of demand-response mechanisms. In this foreseeable framework, proper Hybrid Energy Systems sizing, and operation strategies will be crucial to dictate the electric power system’s contribution to the “green” agenda. This paper presents an optimal power dispatch strategy for grid-connected/off-grid hybrid energy systems with storage capabilities. The Short-Term Price Forecast information as an important decision-making tool for market players will guide the cost side dispatch strategy, alongside with the storage availability. Different scenarios were examined to highlight the effectiveness of the proposed approach.

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“…(2019) [13] defined a graphbased semi-supervised learning approach and introduced large data limits of the probit and Bayesian level set problem formulations. Wang et. al.…”

Section: Introductionmentioning

confidence: 99%

“…al. (2019) [14] have used unsupervised approach integrated with classification process for the prediction of day ahead electricity price. There are several such algorithms available in the literature which are quite effective [15][16].…”

Section: Introductionmentioning

confidence: 99%

Improving Semi-Supervised Classification using Clustering

Arora¹,

Tushir²,

Kashyap³

2018

ICST Transactions on Scalable Information Systems

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Supervised classification techniques, broadly depend on the availability of labeled data. However, collecting this labeled data is always a tedious and costly process. To reduce these efforts and improve the performance of classification process, this paper proposes a new framework, which combines a most basic classification technique with the semi-supervised process of clustering. Semi-supervised clustering algorithms, aim to increase the accuracy of clustering process by effectively exploring available supervision from a limited amount of labeled data and help to label the unlabeled data. In our paper, a semi-supervised clustering is integrated with naive bayes classification technique which helps to better train the classifier. To evaluate the performance of the proposed technique, we conduct experiments on several real world benchmark datasets. The experimental results show that the proposed approach surpasses the competing approaches in both accuracy and efficiency.

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Daily pattern prediction based classification modeling approach for day-ahead electricity price forecasting

Cited by 110 publications

References 43 publications

Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities

Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities

Daily Operation Optimization of a Hybrid Energy System Considering a Short-Term Electricity Price Forecast Scheme

Improving Semi-Supervised Classification using Clustering

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