Short-term Electricity Price Forecasting Research Articles

An Efficient Framework for Short-Term Electricity Price Forecasting in Deregulated Power Market

It is widely acknowledged that electricity price forecasting become an essential factor in operational activities, planning, and scheduling for the participant in the price-setting market, nowadays. Nevertheless, electricity price became a complex signal due to its non-stationary, non-linearity, and time-variant behavior. Consequently, a variety of artificial intelligence techniques are proposed to provide an efficient method for short-term electricity price forecasting. BSA as the recent augmentation of optimization technique, yield the potential of searching a closed-form solution in mathematical modeling with a higher probability, obviating the necessity to comprehend the correlations between variables. Concurrently, this study also developed a feature selection technique, to select the input variables subsets that have a substantial implication on forecasting of electricity price, based on a combination of mutual information (MI) and SVM. For the verification of simulation results, actual data sets from the Ontario energy market in the year 2020 covering various weather seasons are acquired. Finally, the obtained results demonstrate the feasibility of the proposed strategy through improved preciseness in comparison with the distinctive methods.

Short-Term Electricity Price Forecast Using Frequency Analysis and Price Spikes Oversampling

This paper proposes a new short-term electricity price forecast framework using frequency analysis and price spikes oversampling. To perform normal price forecast, firstly, the variational mode decomposition (VMD) method is adopted to decompose price data into multiple frequency band-limited modes where each mode has a center frequency. Then, the extended discrete Fourier transform (EDFT) method is utilized to transform the decomposed price modes from time domain into frequency domain to predict the price. In addition, to facilitate price spike prediction, over-sampling methods including the enhanced structure preserving oversampling (ESPO) and the synthetic minority oversampling technique (SMOTE) for regression, are adopted to synthesize price spike cases. Multiple electricity markets are studied and numerous comparison results illustrate that the proposed forecast framework is capable to accurately predict both normal electricity prices and price spikes.

Optimized Data-Driven Models for Short-Term Electricity Price Forecasting Based on Signal Decomposition and Clustering Techniques

In recent decades, the traditional monopolistic energy exchange market has been replaced by deregulated, competitive marketplaces in which electricity may be purchased and sold at market prices like any other commodity. As a result, the deregulation of the electricity industry has produced a demand for wholesale organized marketplaces. Price predictions, which are primarily meant to establish the market clearing price, have become a significant factor to an energy company’s decision making and strategic development. Recently, the fast development of deep learning algorithms, as well as the deployment of front-end metaheuristic optimization approaches, have resulted in the efficient development of enhanced prediction models that are used for electricity price forecasting. In this paper, the development of six highly accurate, robust and optimized data-driven forecasting models in conjunction with an optimized Variational Mode Decomposition method and the K-Means clustering algorithm for short-term electricity price forecasting is proposed. In this work, we also establish an Inverted and Discrete Particle Swarm Optimization approach that is implemented for the optimization of the Variational Mode Decomposition method. The prediction of the day-ahead electricity prices is based on historical weather and price data of the deregulated Greek electricity market. The resulting forecasting outcomes are thoroughly compared in order to address which of the two proposed divide-and-conquer preprocessing approaches results in more accuracy concerning the issue of short-term electricity price forecasting. Finally, the proposed technique that produces the smallest error in the electricity price forecasting is based on Variational Mode Decomposition, which is optimized through the proposed variation of Particle Swarm Optimization, with a mean absolute percentage error value of 6.15%.

Day-Ahead Electricity Price Forecasting Based on Hybrid Regression Model

Since the deregulation of the power markets, accurate short term Electricity Price Forecasting (EPF) has become crucial in maximizing economic benefits and mitigating power market risks. Due to the challenging characteristics of electricity price, which comprise high volatility, rapid spike, and seasonality, developing robust machine learning prediction tools becomes cumbersome. This work proposes a new hybrid machine learning method for a day-ahead EPF, which involves linear regression Automatic Relevance Determination (ARD) and ensemble bagging Extra Tree Regression (ETR) models. Considering that each model of EPF has its own strengths and weaknesses, combining several models gives more accurate predictions and overcomes the limitations of an individual model. Therefore, the linear ARD model is applied because it can efficiently deal with trend and seasonality variations; on the other hand, the ensemble ETR model is employed to learn from interactions, and thus combining ARD with ETR produces robust forecasting outcomes. The effectiveness of the proposed method was validated using a data set from the Nord Pool electricity market. The proposed model is compared with other models to demonstrate its superiority using performance matrices, such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Experiment results show that the proposed method achieves lower forecasting errors than other individual and hybrid models. Additionally, a comparative study has been performed against previous works, where forecasting measurement of the proposed method outperforms previous works’ accuracy in forecasting electricity price.

Short-Term Electricity Price Forecasting Based on BP Neural Network Optimized by SAPSO

In the electricity market environment, the market clearing price has strong volatility, periodicity and randomness, which makes it more difficult to select the input features of artificial neural network forecasting. Although the traditional back propagation (BP) neural network has been applied early in electricity price forecasting, it has the problem of low forecasting accuracy. For this reason, this paper uses the maximum information coefficient and Pearson correlation analysis to determine the main factors affecting electricity price fluctuation as the input factors of the forecasting model. The improved particle swarm optimization algorithm, called simulated annealing particle swarm optimization (SAPSO), is used to optimize the BP neural network to establish the SAPSO-BP short-term electricity price forecasting model and the actual sample data are used to simulate and calculate. The results show that the SAPSO-BP price forecasting model has a high degree of fit and the average relative error and mean square error of the forecasting model are lower than those of the BP network model and PSO-BP model, as well as better than the PSO-BP model in terms of convergence speed and accuracy, which provides an effective method for improving the accuracy of short-term electricity price forecasting.

A feature extraction- and ranking-based framework for electricity spot price forecasting using a hybrid deep neural network

In deregulated electricity markets, reliable electricity market price forecasting is the foundation for making the bidding strategy, operating dispatch control, and hedging volatility risk. However, electricity prices are high-volatile, nonstationary, multi-seasonal, making it difficult to estimate future trends. This paper proposes a hybrid model integrating a deep learning model, feature extraction and feature selection method to forecast short-term electricity prices. In the proposed framework, the ensemble empirical mode decomposition (EEMD) filter is utilized for multi-dimensional sequences, solving hidden characteristic extraction problems. The constructed feature space is identified and ranked under the max-dependency and min-redundancy (MRMR) criterion, improving the accuracy of feature selection. Finally, combining EEMD and MRMR with bidirectional long short-term memory (BiLSTM), a new hybrid framework is designed to improve the efficiency of short-term electricity price forecasting. Case studies on the PJM and New South Wales electricity markets confirm that our model outperforms alternatives on the forecasting accuracy. The average mean absolute percentage error (MAPE) of the proposed model is reduced by 4% to 21% compared to state-of-the-art models for 1-h and 24-h ahead forecasting. The proposed model has achieved relatively higher stability and adaptability in different forecasting steps and can better capture sophisticated fluctuations in electricity prices.

Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine

Day-ahead electricity price forecasting plays a critical role in balancing energy consumption and generation, optimizing the decisions of electricity market participants, formulating energy trading strategies, and dispatching independent system operators. Despite the fact that much research on price forecasting has been published in recent years, it remains a difficult task because of the challenging nature of electricity prices that includes seasonality, sharp fluctuations in price, and high volatility. This study presents a three-stage short-term electricity price forecasting model by employing ensemble empirical mode decomposition (EEMD) and extreme learning machine (ELM). In the proposed model, the EEMD is employed to decompose the actual price signals to overcome the non-linear and non-stationary components in the electricity price data. Then, a day-ahead forecasting is performed using the ELM model. We conduct several experiments on real-time data obtained from three different states of the electricity market in Australia, i.e., Queensland, New South Wales, and Victoria. We also implement various deep learning approaches as benchmark methods, i.e., recurrent neural network, multi-layer perception, support vector machine, and ELM. In order to affirm the performance of our proposed and benchmark approaches, this study performs several performance evaluation metric, including the Diebold–Mariano (DM) test. The results from the experiments show the productiveness of our developed model (in terms of higher accuracy) over its counterparts.

Multi-Horizon Electricity Load and Price Forecasting Using an Interpretable Multi-Head Self-Attention and EEMD-Based Framework

Accurate system marginal price and load forecasts play a pivotal role in economic power dispatch, system reliability and planning. Price forecasting helps electricity buyers and sellers in an energy market to make effective decisions when preparing their bids and making bilateral contracts. Despite considerable research work in this domain, load and price forecasting still remains to be a complicated task. Various uncertain elements contribute to electricity price and demand volatility, such as changes in weather conditions, outages of large power plants, fuel cost, complex bidding strategies and transmission congestion in the power system. Thus, to deal with such difficulties, we propose a novel hybrid deep learning method based upon bidirectional long short-term memory (BiLSTM) and a multi-head self-attention mechanisms that can accurately forecast locational marginal price (LMP) and system load on a day-ahead basis. Additionally, ensemble empirical mode decomposition (EEMD), a data-driven algorithm, is used for the extraction of hidden features from the load and price time series. Besides that, an intuitive understanding of how the proposed model works under the hood is demonstrated using different interpretability use cases. The performance of the presented method is compared with existing well-known techniques applied for short-term electricity load and price forecast in a comprehensive manner. The proposed method produces considerably accurate results in comparison to existing benchmarks.

Short-term electricity load and price forecasting by a new optimal LSTM-NN based prediction algorithm

Nowadays, a basic commodity for a human being to lead a standard lifestyle with human comfort irrespective of the nature of environmental conditions is electric power. The electricity load demand increases tremendously especially for a metropolitan city due to climatic conditions, population growth, local area development, industries expansion, air pollution, thermal device usage, etc. Hence, the accuracy of electricity load and its price forecasting is a deciding factor for the power distribution network to retain as an efficient, sustainable, and secure consumer-friendly network. On the other hand, based on the volatile, intermittent, and uncertain behavior of electricity load and price, an accurate, and robust forecast model should be designed. In this paper, a new hybrid forecast model for short-term electricity load and price prediction has been developed. The proposed method includes three modules: wavelet transform that is used to eliminate fluctuation behaviors of the electricity load and price time series, feature selection based on entropy and mutual information has been proposed to rank candidate inputs and eliminate redundant inputs according to their information value, and a new learning algorithm. The proposed learning method consists of a deep learning algorithm with LSTM networks which improves the accuracy of predictions. The performance of the proposed method has been validated successfully on load and price data collected from the Pennsylvania-New Jersey-Maryland (PJM) and Spain electricity markets. Also, for further test, the load data in Iran have been used.

A hybrid model based on bidirectional long short-term memory neural network and Catboost for short-term electricity spot price forecasting

Electricity price forecasting plays a crucial role in a liberalised electricity market. Generally speaking, long-term electricity price is widely utilised for investment profitability analysis, grid or transmission expansion planning, while medium-term forecasting is important to markets that involve medium-term contracts. Typical applications of medium-term forecasting are risk management, balance sheet calculation, derivative pricing, and bilateral contracting. Short-term electricity price forecasting is essential for market providers to adjust the schedule of production, i.e., balancing consumers' demands and electricity generation. Results from short-term forecasting are utilised by market players to decide the timing of purchasing or selling to maximise profits. Among existing forecasting approaches, neural networks are regarded as the state of art method due to their capability of modelling high non-linearity and complex patterns inside time series data. However, deep neural networks are not studied comprehensively in this field, which represents a good motivation to fill this research gap. In this article, a deep neural network-based hybrid approach is proposed for short-term electricity price forecasting. To be more specific, categorical boosting (Catboost) algorithm is used for feature selection and a bidirectional long short-term memory neural network (BDLSTM) will serve as the main forecasting engine in the proposed method. To evaluate the effectiveness of the proposed approach, 2018 hourly electricity price data from the Nord Pool market are invoked as a case study. Moreover, the performance of the proposed approach is compared with those of multi-layer perception (MLP) neural network, support vector regression (SVR), ensemble tree, ARIMA as well as two recent deep learning-based models, gated recurrent units (GRU) and LSTM models. A real-world dataset of Nord Pool market is used in this study to validate the proposed approach. Mean percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) are used to measure the model performance. Experiment results show that the proposed model achieves lower forecasting errors than other models considered in this study although the proposed model is more time consuming in terms of training and forecasting.

Conformal prediction interval estimation and applications to day-ahead and intraday power markets

In this study, we investigated the application of the conformal prediction (CP) concept in the context of short-term electricity price forecasting. In particular, we determined the most important aspects related to the utility of CP, as well as explaining why this simple but highly effective idea has proved useful in other application areas and why its characteristics make it promising for short-term power applications. We compared the performance of CP with various state-of-the-art electricity price forecasting models, such as quantile regression averaging, in an empirical out-of-sample study of three short-term electricity time series. We combined CP with various underlying point forecast models to demonstrate its versatility and behavior under changing conditions. Our findings suggest that CP yields sharp and reliable prediction intervals in short-term power markets. We also inspected the effects of each of the model components to provide path-based guideline regarding how to find the best CP model for each market.

Short-term electricity price and load forecasting in isolated power grids based on composite neural network and gravitational search optimization algorithm

Electricity price forecasting is a key aspect for market participants to maximize their economic efficiency in deregulated markets. Nevertheless, due to its non-linearity and non-stationarity, the trend of the price is usually complicated to predict. On the other hand, the accuracy of short-term electricity price and load forecasting is fundamental for an efficient management of electric systems. An accurate prediction can benefit future plans and economic operations of the power systems’ operators. In this paper, a new and accurate combined model has been proposed for short-term load forecasting and short-term price forecasting in deregulated power markets. It includes variational mode decomposition, mix data modeling, feature selection, generalized regression neural network and gravitational search algorithm. A mixed data model for the price and load forecast has been considered and integrated with the original signal series of price and load and their decomposition. Throughout this model, the candidate input variables are chosen by a distinct hybrid feature selection. Two reliable electricity markets (Pennsylvania-New Jersey-Maryland and Spanish electricity markets) have been used to test the proposed forecasting model and the obtained results have been compared with different valid benchmark prediction models. Lastly, the real load data of Favignana Island's power grid have been considered to test the proposed model. The obtained results pinpointed that the proposed model’s precision and stability is higher than in other benchmark forecasting models.

Short-term electricity price forecasting G-LSTM model and economic dispatch for distribution system

In electricity market, electricity prices play an important role. The short-term electricity trading accounts for a large part in the market. Thus, this paper studied the short-term electricity price forecasting and proposed a new method -- G-LSTM (LSTM with error correction using GARCH model). Using this model, we achieve good performance on the LMPs (locational marginal prices) forecasting in the PJM power market. And based on our electricity price forecasting results, we calculate the ED (economic dispatch) plan of an IEEE 33-bus distribution system which contains wind power generation and photovoltaic power generation as an example.

Probability Prediction Method of Short-Term Electricity Price Based on Quantile Neural Network Model

Aiming at the inaccuracy of short-term electricity price forecasting in competitive power markets, a probabilistic short-term electricity price forecasting method based on the quantile neural network model is proposed. First, a method for selecting electricity price similarity based on comprehensive influencing factors is designed to select the forecast data set with similar characteristics to the forecast date. The similar daily quantile regression algorithm is then combined with the generalized dynamic fuzzy neural network to construct a quantile neural network electricity price model for obtaining the predicted daily electricity price condition quantile. Finally, the kernel density function is used to convert the predicted daily electricity price condition quantile into the predicted probability density curve to realize short-term electricity price probability prediction. The data of the electricity market of the city of Dayton, Ohio in the United States is used as an example. The experimental results demonstrate that the proposed method can effectively improve the accuracy of short-term electricity price forecasting.

Short-term electricity price forecast based on long short-term memory neural network

Effective short-term electricity price forecasting is of great significance to reduce bidding risk and obtain stable income. In order to predict the timing price, this paper proposes a short-term electricity price forecasting model based on Long Short-Term Memory (LSTM) neural network. The model uses pauta criterion and Lagrange interpolation polynomial to process the historical data of electricity price, normalize the obtained data and input it into the LSTM network layer, using the Mean Squared Error as the loss function to measure the model forecasting. The effect is based on the loss function, and the weight coefficient of the LSTM neural network is updated by the Adam algorithm. Finally, the timing of the LSTM neural network is predicted. Using the real-time data of the California power market and the real-time data of the US PJM power market, it is proved that the accuracy of this method is higher than that of BP neural network, Cart regression tree and polynomial regression.

An adaptive hybrid model for short term electricity price forecasting

With the large-scale renewable energy integration into the power grid, the features of electricity price has become more complex, which makes the existing models hard to obtain a satisfactory results. Hence, more accurate and stable forecasting models need to be developed. In this paper, a new adaptive hybrid model based on variational mode decomposition (VMD), self-adaptive particle swarm optimization (SAPSO), seasonal autoregressive integrated moving average (SARIMA) and deep belief network (DBN) is proposed for short term electricity price forecasting. The effectiveness of the proposed model is verified by using data from Australian, Pennsylvania-New Jersey-Maryland (PJM) and Spanish electricity markets. Empirical results show that the proposed model can significantly improve the forecasting accuracy and stability.

Some Solutions to the A Priori Knowledge Issue in the Short-Term Electricity Price Forecasting

The recently proposed in the energy literature approach to short-term electricity price forecasting, based on explicit accounting for the long-term price dynamic (i.e. its independent modeling), has demonstrated its efficiency in gaining forecast accuracy. But the practical implementation of this approach has certain impediments, because the true trend-cyclical component is unknown in most cases, while the choice of the method and the degree of smoothing of a time-series to estimate this component can only be made by experts on an a priori basis. If such choice is made incorrectly, this eliminates the mentioned advantage of this approach, and may lead to accuracy loss as compared even to less sophisticated forecasting methods. In the current research we call it the a priori knowledge issue and study some possible solutions of this problem. We show that the adaptive methods of trend estimation, which are based on different algorithms of the empirical mode decomposition, while not requiring any a priori setups, still, do not solve the studied issue. In turn, forecast combining conducted for individual models (based on different methods and degrees of time-series smoothing) allows not only to mitigate the need of making a priori choices, but also has lower forecast error and, thus, performs better than individual models. We also propose a new approach to forecast combining (based on p-values of a model confidence set (MCS)) and show that it outperforms a number of well-established classic forecast averaging schemes (simple averaging, constrained OLS, inverted root mean square errors). Finally, our research indicates that making an MCS-based trimming of the pool of models before averaging of their forecasts does not lead to lower prediction errors relative to their untrimmed averaging. Hence, conducting such trimming does not provide any extra advantages in solving the a priori knowledge issue.

A hybrid short-term electricity price forecasting framework: Cuckoo search-based feature selection with singular spectrum analysis and SVM

Under the liberalization and deregulation of the power industry, price forecasting has become a cornerstone for market participants' decision-making such as bidding strategies and purchase plans. However, the exclusive nonlinearity dynamics of electricity price is a challenge problem that largely affects forecasting accuracy. To address this task, this paper presents a hybrid forecasting framework for short-term electricity price forecasting by exploiting and mining the important information hidden in the electricity price signal. Moreover, a hybrid feature selection method (HFS) is introduced into the forecasting strategy. To exhibit the dynamical characteristics of electricity price, we primarily perform a singular spectrum analysis (SSA)-based systematic analysis process by using the merit of SSA and analyzing the multiple seasonal patterns of short-term electricity price series, providing a meaningful representation of the hidden patterns and time-varying volatility of electricity price series. Aiming at selecting the key features, the candidate variables are constructed considering the dynamic behavior of price series; further, to capture the optimal features from the candidates, the correlation threshold θ is defined for the adjustable parameters in HFS and optimally determined by the intelligent search algorithm. Additionally, triangulation based on the Pearson, Spearman and Kendall rank correlation coefficient is performed to strengthen the reliability of the proposed method. The proposed hybrid forecasting framework is validated in the New South Wales electricity market, which demonstrates that the developed approach is truly better than the benchmark models used and a reliable and promising tool for short-term electricity price forecasting.

Understanding intraday electricity markets: Variable selection and very short-term price forecasting using LASSO

We use a unique set of prices from the German EPEX market and take a closer look at the fine structure of intraday markets forelectricity, with their continuous trading for individual load periods up to 30 min before delivery. We apply the least absolute shrinkage and selection operator (LASSO) in order to gain statistically sound insights on variable selection and provide recommendations for very short-term electricity price forecasting.

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

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.