Electricity price forecasting plays a fundamental role in ensuring efficient market operation and informed decision making. With the growing integration of renewable energy, prices have become more volatile and difficult to predict, increasing the ne...
We compare the 24-hour ahead forecasting performance of two methods commonly used for the prediction of the power output of photovoltaic systems. Both methods are based on Artificial Neural Networks (ANN), which have been trained on the same dataset,...
Accurate day-ahead solar forecasting is essential for grid stability and energy planning. This study introduces a specialized forecasting framework that enhances accuracy by training models on specific day-to-day sky condition transitions. The framew...
At present, due to the errors of wind power, solar power and various types of load forecasting, the optimal scheduling results of the integrated energy system (IES) will be inaccurate, which will affect the economic and reliable operation of the inte...
Forecasting hourly spot prices for real-time electricity markets is a key activity in economic and energy trading operations. This paper proposes a novel two-stage approach that uses a combination of Auto-Regressive Integrated Moving Average (ARIMA)...
With the large-scale adoption of photovoltaic (PV) systems as a renewable energy source, accurate long-term forecasting benefits both utilities and customers. However, developing forecasting models is challenging due to the need for high-quality trai...
A main challenge for integrating the intermittent photovoltaic (PV) power generation remains the accuracy of day-ahead forecasts and the establishment of robust performing methods. The purpose of this work is to address these technological challenges...
To improve the accuracy of the day-ahead load forecasting predictions of a single model, a novel modular parallel forecasting model with feature selection was proposed. First, load features were extracted from a historic load with a horizon from the...
As artificial neural network architectures grow increasingly more efficient in time-series prediction tasks, their use for day-ahead electricity price and demand prediction, a task with very specific rules and highly volatile dataset values, grows mo...
Accurate very short-term load forecasting (VSTLF) is critical to ensure a secure operation of power systems under increasing uncertainty due to renewables. This study proposes an eXtreme Gradient Boosting (XGBoost)-based VSTLF model that incorporates...
In day-ahead electricity price forecasting (EPF) variable selection is a crucial issue. Conducting an empirical study involving state-of-the-art parsimonious expert models as benchmarks, datasets from three major power markets and five classes of aut...
Accurate day-ahead photovoltaics (PV) power forecasting results are significant for power grid operation. According to different weather modes, the existing research has established a classification forecast framework to improve the accuracy of day-a...
Over the last 30 years, day-ahead electricity price forecasts have been critical to public and private decision-making. This importance has increased since the global wave of deregulation and liberalization in the energy sector at the end of the 1990...
Participants in deregulated electricity markets face risks from price volatility due to various factors, including fuel prices, renewable energy production, electricity demand, and crises such as COVID-19 and energy-related issues. Price forecasting...
The role of renewable energy sources in the Polish power system is growing. The highest share of installed capacity goes to wind and solar energy. Both sources are characterized by high variability of their power output and very low dispatchability....
Grid operators of islands with limited system tolerance are often challenged by the need to curtail wind energy in order to maintain system stability and security of supply. At the same time, and in the absence of storage facilities and/or other mean...
In this paper day-ahead electricity price forecasting for the Denmark-West region is realized with a 24 h forecasting range. The forecasting is done for 212 days from the beginning of 2017 and past data from 2016 is used. For forecasting, Autoregress...
In the present liberalized energy markets, electricity demand forecasting is critical for planning of generation capacity and required resources. An accurate and efficient electricity demand forecast can reduce the risk of power outages and excessive...
Volatility in the modern world and electricity Day-Ahead Markets (DAMs) usually makes long-term historical data irrelevant or even detrimental for accurate forecasting. This study directly addresses this challenge by proposing a novel forecasting par...
Day-ahead electricity price forecasting (DAEPF) holds critical significance for stakeholders in energy markets, particularly in areas with large amounts of renewable energy sources (RES) integration. In Japan, the proliferation of RES has led to inst...
Short-term load forecasting is integral to the energy planning sector. Various techniques have been employed to achieve effective operation of power systems and efficient market management. We present a scalable system for day-ahead household electri...
The increase of energy consumption in the world is reflected in the consumption of natural gas. However, this increment requires additional investment. This effect leads imbalances in terms of demand forecasting, such as applying penalties in the cas...
Electricity price forecasting (EPF) has become an essential part of decision-making for energy companies to participate in power markets. As the energy mix becomes more uncertain and stochastic, this process has also become important for industrial c...
Park-level integrated energy systems (IESs) are increasingly challenged by rapid electrification and higher penetration of renewable energy, which exacerbate source–load imbalances and scheduling uncertainty. This study proposes a unified frame...
The incorporation of renewable energy systems in the world energy system has been steadily increasing during the last few years. In terms of the building sector, the usual consumers are becoming increasingly prosumers, and the trend is that communiti...
Electric power load forecasting is essential for the efficient operation and strategic planning of utilities. Decisions regarding the electric market, power generation, load management, and infrastructure development all rely on accurate load predict...
In the operation of a smart grid (SG), day-ahead load forecasting (DLF) is an important task. The SG can enhance the management of its conventional and renewable resources with a more accurate DLF model. However, DLF model development is highly chall...
Accurate solar PV power forecasting can provide expected future PV output power so as to help the system operator to dispatch traditional power plants to maintain the balance between supply and demand sides. However, under non-stationary weather cond...
Forecasting day-ahead electricity prices is a crucial research area. Both wholesale and retail sectors highly value improved forecast accuracy. Renewable energy sources have grown more influential and effective in the US power market. However, curren...
Day-ahead electricity price forecasting (DAEPF) is vital for participants in energy markets, particularly in regions with high integration of renewable energy sources (RESs), where price volatility poses significant challenges. The accurate forecasti...
The increasing penetration rate of electric vehicles, associated with a growing charging demand, could induce a negative impact on the electric grid, such as higher peak power demand. To support the electric grid, and to anticipate those peaks, a gro...
Recently, the development in combining point forecasts of electricity prices obtained with different length of calibration windows have provided an extremely efficient and simple tool for improving predictive accuracy. However, the proposed methods a...
An accurate forecast of the exploitable energy from Renewable Energy Sources is extremely important for the stability issues of the electric grid and the reliability of the bidding markets. This paper presents a comparison among different forecasting...
With the rapid expansion of renewable energy, the penetration rate of behind-the-meter (BTM) solar photovoltaic (PV) generators is increasing in South Korea. The BTM solar PV generation is not metered in real-time, distorts the electric load and incr...
Since the beginning of this century, the share of renewables in Europe’s total power capacity has almost doubled, becoming the largest source of its electricity production. In 2015 alone, photovoltaic (PV) energy generation rose with a rate of more t...
The importance of electricity in people’s daily lives has made it an indispensable commodity in society. In electricity market, the price of electricity is the most important factor for each of those involved in it, therefore, the prediction of the e...
With the rapid increase in renewable energy penetration and the expansion of multi-regional interconnected power systems, there is a growing need to forecast the power output of renewable energy power plant clusters within a region. Existing methods...
Germany has experienced rapid growth in onshore wind capacities over the past two decades. Substantial capacities of offshore wind turbines have been added since 2013. On a local, highly-resolved level, this analysis evaluated if differences in wind...
Recent studies suggest that decomposing a series of electricity spot prices into a trend-seasonal and a stochastic component, modeling them independently, and then combining their forecasts can yield more accurate predictions than an approach in whic...
With increasing renewable energy generation capacities connected to the power grid, a number of decision-making problems require some form of consistency in the forecasts that are being used as input. In everyday words, one expects that the sum of th...
We conduct an extensive empirical study on the selection of calibration windows for day-ahead electricity price forecasting, which involves six year-long datasets from three major power markets and four autoregressive expert models fitted either to r...
The paper proposes a novel hybrid feature selection (FS) method for day-ahead electricity price forecasting. The work presents a novel hybrid FS algorithm for obtaining optimal feature set to gain optimal forecast accuracy. The performance of the pro...
The aim of this paper is to address the challenges regarding the safety and economics of power system operation after the integration of a high proportion of wind power. In response to the limitations of the literature, which often fails to simultane...
This paper presents the analysis of the importance of a set of explanatory (input) variables for the day-ahead price forecast in the Iberian Electricity Market (MIBEL). The available input variables include extensive hourly time series records of wea...
The evolution of electricity markets has led to increasingly complex energy trading dynamics and the integration of renewable energy sources as well as the influence of several external market factors contributed towards price volatility. Therefore,...
Forecasting the hourly spot price of day-ahead and intraday markets is particularly challenging in electric power systems characterized by high installed capacity of renewable energy technologies. In particular, periods with low and high price levels...
This paper proposes a new hybrid model to forecast electricity market prices up to four days ahead. The components of the proposed model are combined in two dimensions. First, on the “vertical” dimension, long short-term memory (LSTM) neu...
Recently, a dynamic development of intermittent renewable energy sources (RES) has been observed. In order to allow for the adoption of trading contracts for unplanned events and changing weather conditions, the day-ahead markets have been complement...
Electricity load forecasting is a crucial undertaking within all the deregulated markets globally. Among the research challenges on a global scale, the investigation of deep transfer learning (DTL) in the field of electricity load forecasting represe...
Accurate day-ahead photovoltaic (PV) power forecasting is essential for secure operation and scheduling in power systems with high PV penetration, yet its performance is often constrained by the coarse spatial resolution of operational numerical weat...
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