The Economic Viability of PV Power Plant Based on a Neural Network Model of Electricity Prices Forecast: A Case of a Developing Market

Mišnić, Nikola; Pejović, Bojan; Jovović, Jelena; Rogić, Sunčica; Đurišić, Vladimir

doi:10.3390/en15176219

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…In 2021, solar energy represented 3.6% of total electricity generation. Mišnić et al (2022) made a financial analysis of a project involving the economic viability of a 5MV solar power plant in Montenegro. The electricity price was predicted by ARIMA and neural networks.…”

Section: Discussionmentioning

confidence: 99%

Understanding Electricity Price Evolution – Day-Ahead Market Competitiveness in Romania

Bârã

Oprea

Georgescu

2023

Journal of Business Economics and Management

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The unexpected pandemic eruption in March 2020, the European efforts to diminish the gas house emissions, prolonged drought, higher inflation and the war in Ukraine clearly have had a strong impact on the electricity price. In this paper, we analyze the electricity prices on the Romanian Day-Ahead Market (DAM) along with other variables (inflation, consumption and traded volume of gas on DAM) over the last three and a half years in an attempt to understand its evolution and future trend in the economic and geopolitical context. Autoregressive Distributed Lag models are proposed to analyze the causality among variables on short- and long-term perspective, whereas Quantile Regression (QR) is proposed to enhance the results of the Ordinary Least Squares (OLS) regression. Furthermore, using market concentration metrics – Herfindahl-Hirschman Index (HHI), C1 and C3 ratio, we analyze the competitiveness on the Romanian DAM and correlate it with the electricity price evolution. The concentration indicators on this market reflect the degree of competition manifested between sellers and buyers respectively, their dynamics being able to influence the price level. The higher concentration on the sellers’ side (HHI = 1500) indicates a potential speculative behavior on this market that led to higher prices on DAM.

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Section: Discussionmentioning

confidence: 99%

Understanding Electricity Price Evolution – Day-Ahead Market Competitiveness in Romania

Bârã

Oprea

Georgescu

2023

Journal of Business Economics and Management

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“…High volatility of electricity prices results in peaks and troughs occurring unexpectedly, and supply and demand fluctuations are constantly being altered. ARIMA was suggested by Mišnić et al [10], where a linear statistical model calculates the pattern between electricity prices to make predictions. The SVM was proposed by Prahara et al [11] following the principle of structural risk minimization to predict the electricity price.…”

Section: Related Workmentioning

confidence: 99%

Day-Ahead Electricity Price Forecasting Based on GCM Filtering and Higher-Order Pooling Feature Enhancement

Sun,

Wang,

et al. 2023

IEEE Access

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Redundant complexities and inadequate representation of spatiotemporal features are included in the electricity price data. To address complex data redundancy and inadequate representation of spatiotemporal features, a gated channel mechanism (GCM) combined with a high-order pooling feature enhanced convolutional LSTM network (GCHCon-LSTM) electricity price prediction model is proposed. On the standardized processed electricity price dataset, the data vertical correlation information is expanded using gated dual convolutional neural network (GDCNN) integrated adjustment features. Redundant features are filtered using GCM. Temporal and spatial features are extracted by LSTM and ASPConv. Key information on spatial features is extracted using higher-order pooling. Combined with temporal features, the spatiotemporal feature representation is enhanced in a time-dominant and spatial manner. The prediction result is obtained by error correction. On the ERCOT Houston area electricity price dataset, compared to LSTM, CNN-LSTM, GHTnet and ILRCN, the experimental results showed that MAE, MSE and RMSE are reduced by the highest 21.50%, 29.56% and 40.18%, respectively, and the lowest 7.95%, 6.39% and 13.60%.INDEX TERMS Electricity price forecasting, GDCNN, Gated channel mechanism, Higher-order pooling enhancement, LSTM

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The State of the Art Electricity Load and Price Forecasting for the Modern Wholesale Electricity Market

Laitsos,

Vontzos,

Paraschoudis

et al. 2024

Energies

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In a modern and dynamic electricity market, ensuring reliable, sustainable and efficient electricity distribution is a pillar of primary importance for grid operation. The high penetration of renewable energy sources and the formation of competitive prices for utilities play a critical role in the wider economic development. Electricity load and price forecasting have been a key focus of researchers in the last decade due to the substantial economic implications for both producers, aggregators and end consumers. Many forecasting techniques and methods have emerged during this period. This paper conducts a extensive and analytical review of the prevailing load and electricity price forecasting methods in the context of the modern wholesale electricity market. The study is separated into seven main sections. The first section provides the key challenges and the main contributions of this study. The second section delves into the workings of the electricity market, providing a detailed analysis of the three markets that have evolved, their functions and the key factors influencing overall market dynamics. In the third section, the main methodologies of electricity load and price forecasting approaches are analyzed in detail. The fourth section offers a comprehensive review of the existing literature focusing on load forecasting, highlighting various methodologies, models and their applications in this field. This section emphasizes the advances that have been made in all categories of forecasting models and their practical application in different market scenarios. The fifth section focuses on electricity price forecasting studies, summarizing important research papers investigating various modeling approaches. The sixth section constitutes a fundamental discussion and comparison between the load- and price-focused studies that are analyzed. Finally, by examining both traditional and cutting-edge forecasting methods, this review identifies key trends, challenges and future directions in the field. Overall, this paper aims to provide an in-depth analysis leading to the understanding of the state-of-the-art models in load and price forecasting and to be an important resource for researchers and professionals in the energy industry. Based on the research conducted, there is an increasing trend in the use of artificial intelligence models in recent years, due to the flexibility and adaptability they offer for big datasets, compared to traditional models. The combination of models, such as ensemble methods, gives us very promising results.

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The Economic Viability of PV Power Plant Based on a Neural Network Model of Electricity Prices Forecast: A Case of a Developing Market

Cited by 3 publications

References 34 publications

Understanding Electricity Price Evolution – Day-Ahead Market Competitiveness in Romania

Understanding Electricity Price Evolution – Day-Ahead Market Competitiveness in Romania

Day-Ahead Electricity Price Forecasting Based on GCM Filtering and Higher-Order Pooling Feature Enhancement

The State of the Art Electricity Load and Price Forecasting for the Modern Wholesale Electricity Market

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