Different price forecasting techniques and their application in deregulated electricity market: A comprehensive study

Pandey, Nivedita; Upadhyay, K. G.

doi:10.1109/iceteeses.2016.7581342

Cited by 16 publications

(9 citation statements)

References 24 publications

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“…The state of the art analysis presented partly in this article and in the previously published article [1] focuses on practical aspects of the application of forecasting methods such as the number of input parameters, forecasting error, and the market on which the model was tested. Based on the collected data from [1] and [14][15][16][17], a number of statistical indicators were obtained that could provide certain guidelines for making conclusions related to the problem of predicting electricity prices. This simple quantitative analysis was a motivation for a more complex analysis that analyzed in detail scientific articles on electricity price forecasting published in IEEE Xplore and Science Direct databases.…”

Section: Of 32mentioning

confidence: 99%

“…Part of the research was presented in the authors' previous article [1] and for this research an additional analysis of the articles was made using the same methodology ( Figure 3). The analysis observed the following characteristics: [14], the main feature of their analysis was the categorization of research papers according to the price forecasting methods classification presented in Figure 1. An extensive analysis of state of the art was made by Weron [15], where each of the price forecasting models was explained in detail with all the typical applications in previously published works.…”

Section: Of 32mentioning

confidence: 99%

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HIRA Model for Short-Term Electricity Price Forecasting

Cerjan¹,

Petricic²,

Delimar

2019

Energies

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In competitive power markets, electric utilities, power producers, and traders are exposed to increased risks caused by electricity price volatility. The growing reliance on renewable sources and their dependence on weather, nuclear uncertainty, market coupling, and global financial instability are contributing to the importance of accurate electricity price forecasting. Since power markets are not all equally developed, different price forecasting methods have been introduced for individual markets. The aim of this research is to introduce a short-term electricity price forecasting method that addresses the problems of price volatility, a varying number of input parameters, varying data availability, and a large number of parameters and input data. Furthermore, the proposed model can be used on any market as it targets the characteristics and specifics of each market. The proposed Hybrid Iterative Reactive Adaptive (HIRA) method consists of two phases. In analysis phase, fundamental parameters which affect the electricity price are identified depending on market development. Obtained parameters are used as data inputs for price forecasting using a hybrid method. The HIRA model combines a statistical approach for large data set analysis and a similar day method with neural network tools. Similar days are examined using a statistical method which combines correlation significance, price volatility, and forecasting accuracy of the historical data. Data are collected based on their availability and electricity prices are forecasted in several iterations. All relevant data for price forecasting are collected, categorized, and arranged using simple indicators which makes the HIRA model adaptive and reactive to new market circumstances. The proposed model is validated using the Hungarian Power Exchange (HUPX) electricity price data records. The results show that with HIRA model forecasting, the error is stable and does not depend on price volatility. The HIRA method has proven to be applicable for forecasting electricity prices in real-time market conditions and enables effective hedging of price risk in the production or market portfolio.

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Section: Of 32mentioning

confidence: 99%

Section: Of 32mentioning

confidence: 99%

HIRA Model for Short-Term Electricity Price Forecasting

Cerjan¹,

Petricic²,

Delimar

2019

Energies

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“…Note, that the problem of forecasting processes is relevant in solving many problems related to optimizing power systems. The extensive scientific literature is devoted to this problem [41][42][43][44][45][46][47][48]. The approach we use is only one of the possible approaches.…”

Section: Implementation Of the First Taskmentioning

confidence: 99%

Estimating the Economic Impacts of Net Metering Schemes for Residential PV Systems with Profiling of Power Demand, Generation, and Market Prices

et al. 2018

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This article analyses the influence of supporting scheme variants on the profitability of a projected investment of residential photovoltaic systems. The focus of the paper lies in evaluating the feasibility for the power system of solar power generation technologies to achieve a balance between energy generation and support costs in a more efficient way. The case study is based on a year-long time series of examples with an hourly resolution of electricity prices from the Nord Pool power market, in addition to the power demand and solar generation of Latvian prosumers. Electric energy generation and the consumption of big data from more than 100 clients were collected. Based on these data, we predict the processes for the next 25 years, and we estimate economic indicators using a detailed description of the net metering billing system and the Monte-Carlo method. A recommendation to change the current net system to a superior one, taking into account the market cost of energy, concludes the paper.

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“…Time-series price forecasting is influenced by several market data factors (system marginal price, load, generators, etc.). Pandey and Upadhyay [4] outlined that price fluctuation is very normal as a result of economic and technical elements. They examined the key factors and noted that demand was the main contributor since the price fluctuates when demand varies.…”

Section: Factors Influencing Energy Tradingmentioning

confidence: 99%

“…The NARMAX system identification technique is used to obtain a model based on measures of the system inputs and outputs and when the data shows nonlinear traits since information regarding past error can be incorporated into this model to help enhance future prediction [3]. When predicting electricity prices for energy trading there are other factors (for example, demand, fuel costs, weather [4]), as well as historical electricity price, that need to be considered as input parameters in the forecasting model. Along with input factors, another important feature is the length of the prediction window.…”

Section: Introductionmentioning

confidence: 99%

Daily Energy Price Forecasting Using a Polynomial NARMAX Model

McHugh

Coleman

Kerr

et al. 2018

Advances in Intelligent Systems and Computing

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Energy prices are not easy to forecast due to nonlinearity from seasonal trends. In this paper a Nonlinear AutoRegressive Moving Average model with eXogenous input (NARMAX model) is created using nonlinear energy price data. To investigate if a short-term forecasting model is capable of predicting energy prices a model was developed using daily data from 2017 over a period of five weeks: observing 1 input lag prediction up to 12 input lag prediction for low-order polynomials (linear, quadratic, and cubic). Various input factors were explored (energy demand and previous price) with different combinations to observe which factors, if any, had an impact on the current price prediction. The results show that the generated NARMAX model is good at describing the inputoutput relationship of energy prices. The model works best with a low-order input regression parameter and linear polynomial degree. It was also noted that including energy demand as an input factor slightly improves the model validation results suggesting that there is a relationship between demand and energy prices.

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Different price forecasting techniques and their application in deregulated electricity market: A comprehensive study

Cited by 16 publications

References 24 publications

HIRA Model for Short-Term Electricity Price Forecasting

HIRA Model for Short-Term Electricity Price Forecasting

Estimating the Economic Impacts of Net Metering Schemes for Residential PV Systems with Profiling of Power Demand, Generation, and Market Prices

Daily Energy Price Forecasting Using a Polynomial NARMAX Model

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