Dynamic Hybrid Model for Short-Term Electricity  Price Forecasting

Cerjan, Marin; Matijaš, Marin; Delimar, Marko

doi:10.3390/en7053304

Cited by 39 publications

(15 citation statements)

References 23 publications

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“…Therefore, the forecast on power load has become an important research focus and has been investigated continuously in recent years, from which power suppliers and consumers can benefit to develop better energy management. The forecasting period ranges from minutes to years due to the various demands for power load forecasting [5][6][7]. Many models have been developed to address this problem, and most of them can be classified into three categories: regression models, time series analysis, and artificial neural networks (ANNs).…”

Section: Introductionmentioning

confidence: 99%

Recurrence Interval Analysis on Electricity Consumption of an Office Building in China

2018

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Abstract:The energy management of office buildings has been a rising concern for owners, researchers, and energy suppliers. The volatility of power load in office buildings threatens energy consumption and risks device security. This paper investigates the load fluctuation patterns in an office building based on user data, using recurrence interval analysis for different thresholds. The recurrence intervals of volatility are fitted by stretched exponential distribution, from which the probability density function is derived. Then, the short-term and long-term memory effect on the fluctuations are learned by conditional probability density function and multifractal detrended fluctuation analysis, respectively. A hazard function is further established to analyze the risk estimation of load volatility and derive the value at risk (VaR). Thus, a functional relationship has been established between average recurrence interval and threshold. The methodology and analysis results addressed in this paper help to understand load fluctuation patterns and aid in the design of energy consumption strategies in office buildings. According to the results of our research, conclusions and management suggestions are provided at the end of this paper.

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Section: Introductionmentioning

confidence: 99%

Recurrence Interval Analysis on Electricity Consumption of an Office Building in China

2018

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“…This parameter can be constant [27] or can be driven by deterministic seasonal variables [28]. Recently, in [22], a nonlinear variant of the autoregressive conditional hazard model has been used to estimate the probability of a spike with a short-term horizon, and in [29], a spike component is predicted in the short term using a linear approximation based on consumption and wind.…”

Section: Previous Workmentioning

confidence: 99%

Medium-Term Probabilistic Forecasting of Extremely Low Prices in Electricity Markets: Application to the Spanish Case

2016

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Abstract:One of the most relevant challenges that have arisen in electricity markets during the last few years is the emergence of extremely low prices. Trying to predict these events is crucial for market agents in a competitive environment. This paper proposes a novel methodology to simultaneously accomplish punctual and probabilistic hourly predictions about the appearance of extremely low electricity prices in a medium-term scope. The proposed approach for making real ex ante forecasts consists of a nested compounding of different forecasting techniques, which incorporate Monte Carlo simulation, combined with spatial interpolation techniques. The procedure is based on the statistical identification of the process key drivers. Logistic regression for rare events, decision trees, multilayer perceptrons and a hybrid approach, which combines a market equilibrium model with logistic regression, are used. Moreover, this paper assesses whether periodic models in which parameters switch according to the day of the week can be even more accurate. The proposed techniques are compared to a Markov regime switching model and several naive methods. The proposed methodology empirically demonstrates its effectiveness by achieving promising results on a real case study based on the Spanish electricity market. This approach can provide valuable information for market agents when they face decision making and risk-management processes. Our findings support the additional benefit of using a hybrid approach for deriving more accurate predictions.

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“…Due to the constant evolution of the EM environment, including the introduction of new players [10] and changes in EM operation, it becomes essential for professionals in this area to completely understand the markets' principles and how to evaluate their investments under such a competitive environment [11]. The use of simulation tools has grown with the need for understanding those mechanisms and how the involved players' interaction affects the outcomes of the markets [12].…”

Section: Introductionmentioning

confidence: 99%

Decision Support for Energy Contracts Negotiation with Game Theory and Adaptive Learning

et al. 2015

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This paper presents a decision support methodology for electricity market players' bilateral contract negotiations. The proposed model is based on the application of game theory, using artificial intelligence to enhance decision support method's adaptive features. This model is integrated in AiD-EM (Adaptive Decision Support for Electricity Markets Negotiations), a multi-agent system that provides electricity market players with strategic behavior capabilities to improve their outcomes from energy contracts' negotiations. Although a diversity of tools that enable the study and simulation of electricity markets has emerged during the past few years, these are mostly directed to the analysis of market models and power systems' technical constraints, making them suitable tools to support decisions of market operators and regulators. However, the equally important support of market negotiating players' decisions is being highly neglected. The proposed model contributes to overcome the existing gap concerning effective and realistic decision support for electricity market negotiating entities. The proposed method is validated by realistic electricity market simulations using real data from the Iberian market operator-MIBEL. Results show that OPEN ACCESSEnergies 2015, 8 9818 the proposed adaptive decision support features enable electricity market players to improve their outcomes from bilateral contracts' negotiations.

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Dynamic Hybrid Model for Short-Term Electricity Price Forecasting

Cited by 39 publications

References 23 publications

Recurrence Interval Analysis on Electricity Consumption of an Office Building in China

Recurrence Interval Analysis on Electricity Consumption of an Office Building in China

Medium-Term Probabilistic Forecasting of Extremely Low Prices in Electricity Markets: Application to the Spanish Case

Decision Support for Energy Contracts Negotiation with Game Theory and Adaptive Learning

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