Electricity Price Forecasting Research Articles

Transfer learning for electricity price forecasting

Electricity price forecasting is an essential task in all the deregulated markets of the world. The accurate prediction of day-ahead electricity prices is an active research field and available data from various markets can be used as input for forecasting. A collection of models have been proposed for this task, but the fundamental question on how to use the available big data is often neglected. In this paper, we propose to use transfer learning as a tool for utilizing information from other electricity price markets for forecasting. We pre-train a neural network model on source markets and finally do a fine-tuning for the target market. Moreover, we test different ways to use the rich input data from various electricity price markets to forecast 24 steps ahead in hourly frequency. Our experiments on four different day-ahead markets indicate that transfer learning improves the electricity price forecasting performance in a statistically significant manner. Furthermore, we compare our results with state-of-the-art methods in a rolling window scheme to demonstrate the performance of the transfer learning approach. Our method improves the performance of the state-of-the-art algorithms by 7% for the French market and 3% for the German market.

Short-term electricity price forecasting based on similarity day screening, two-layer decomposition technique and Bi-LSTM neural network

Electricity price forecasting (EPF) has been challenged by the widespread grid integration of renewable energy (RE), so it is critical to develop a highly accurate and reliable EPF model. In this study, novel considerations of RE generation factors are made, and a quantitative model for the impact of RE on electricity prices is built using random forests (RF) and improved Mahalanobis Distance (IMD). To reduce data duplication, similar days of EPF are first selected. Then it is suggested to decompose the electricity price series into multiple intrinsic mode functions (IMF) and residuals with different frequencies using a two-layer decomposition model based on improved comprehensive ensemble empirical mode decomposition (ICEEMD) and variational mode decomposition (VMD), in order to reduce data noise and volatility. Finally, EPF model based on Bi-directional long short-term memory (Bi-LSTM) is established to forecast multiple subsequences, and the final price forecasting result is obtained after integrated processing. Experimental results show that the RF-IMD-ICEEMD-VMD-Bi-LSTM hybrid model can significantly improve the forecasting performance, reduce the prediction error of EPF, and has the best performance among the comparison models.

Electricity Price Forecasting in New Zealand: A Comparative Analysis of Statistical and Machine Learning Models with Feature Selection

Electricity Price Forecasting in New Zealand: A Comparative Analysis of Statistical and Machine Learning Models with Feature Selection

Electricity price forecast based on the STL-TCN-NBEATS model

Taking long-term high-frequency electricity price data as the research content, this paper proposes seasonal and trend decomposition using loess-temporal convolutional network-neural basis expansion analysis for an interpretable time series forecasting (STL-TCN-NBEATS) model to solve the problems of low forecast accuracy caused by high volatility, high frequency and nonlinearity and poor interpretability of the deep learning model. By comparing the forecast effects of the temporal convolutional network-long short-term memory (TCN-LSTM), LSTM and other models, the main conclusions are as follows: (1) The hybrid model, STL-TCN-NBEATS, selected in this paper can effectively solve the problem of low forecast accuracy after reasonable selection of model parameters. The evaluation indexes of the root mean square error (RMSE) and the mean absolute percentage error (MAPE) were 3.7441 and 4.5044, respectively, which were 3.1416 and 2.1336 lower than those of the second-best model (TCN-LSTM). Compared with the autoregressive integrated moving average model (ARIMA), the accuracy was improved by approximately 49.18% (RMSE) and 60.35% (MAPE). (2) The STL-TCN-NBEATS model has better feature extraction ability, so it can obtain higher forecast accuracy. Since the construction of the TCN introduces extended causal convolution and residual blocks, the deep learning network model has better processing ability and robustness for large sample time series. Moreover, the NBEATS network structure enables the model to be trained quickly, and the experimental results verify the effectiveness and high accuracy of this method. (3) The model not only has high precision but also has some interpretability. By decomposing time series data into a trend term, period term and remainder term, the NBEATS and the TCN are used to process the trend term, period term and remainder term, respectively, so that the hybrid model can forecast electricity prices according to the traditional time series processing mode.

Toward Holistic Energy Management by Electricity Load and Price Forecasting: A Comprehensive Survey

Toward Holistic Energy Management by Electricity Load and Price Forecasting: A Comprehensive Survey

Probabilistic forecasting of electricity prices using an augmented LMARX-model

Probabilistic forecasting of electricity prices using an augmented LMARX-model

Multi-Task Learning for Electricity Price Forecasting and Resource Management in Cloud Based Industrial IoT Systems

Cloud computing has gained immense popularity in the logistics industry. This innovative technology optimizes computing operations by eliminating the requirement for physical equipment for calculations. Instead, specialized companies provide cloud-based computing services, relying heavily on computers and servers that consume substantial amounts of energy. Hence, ensuring the availability of affordable and dependable electricity is paramount for the efficient design and management of these logistics services. Cloud centers, which are power-intensive, face the challenge of reducing their energy consumption due to escalating power costs. To address this issue, efficient data placement and node management strategies are commonly employed in logistics operations. An AlexNet model has been designed to optimize storage relocation and predict power prices. The outcome of this initiative has resulted in a considerable reduction in energy consumption at data centres. The model uses Dwarf Mongoose Optimization Algorithm (DMOA) to produce an optimal solution for the AlexNet and increase its performance with a real-world dataset from IESO in Ontario, Canada. 75% of the available data was used for training to assure the model’s precision, with the remaining 25% allocated to testing purposes. The model forecasts power prices with an MAE of 2.22% and an MSE of 6.33%, resulting in an average reduction of 22.21% in electricity expenses. Our proposed method has an accuracy of 97% compared to 11 benchmark algorithms, including CNN, DenseNet, and SVM having an accuracy of 89%, 88%, and 82%, respectively.

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

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

Multivariable short-term electricity price forecasting using artificial intelligence and multi-input multi-output scheme

In deregulated power markets, electricity price forecasting is the most valuable tool. However, with inherent electricity price characteristics, such as high frequency and volatility, constructing an electricity price forecasting model remains a difficult task for decision-makers and scholars. Accurate electricity price point forecasting (PF) can guide market participants in maximizing benefits. Moreover, appropriate interval forecasting (IF) can provide further information based on PF. Accordingly, a novel electricity price multi-bi-forecasting system using multivariable and multi-input multi-output structures is formulated. The system has three stages: data preprocessing, combination forecasting, and performance evaluation. The data preprocessing stage removes and smooths the high-frequency electricity price and load data. Because the load series has a more regular cycle and smoother fluctuation than the electricity price series, two variables, electricity price and load, are employed for forecasting using a multivariable data arrangement rolling forecast mechanism. In addition, a multi-input multi-output structure is utilized by three member models (back propagation, bidirectional long short-term memory, and gated recurrent unit) to derive PF and IF results concurrently. The final results are obtained using a combined strategy based on the multi-objective salp swarm algorithm. Finally, three experiments are conducted in the Australian electricity market to evaluate the proposed system quantitatively. Results show that the designed system has superior ability in forecasting electricity price and practical application in real situations.

Hourly electricity price forecast for short-and long-term, using deep neural networks

Abstract Despite the practical importance of accurate long-term electricity price forecast with high resolution - and the significant need for that - only small percentage of the tremendous papers on energy price forecast attempted to target this topic. Its reason can be the high volatility of electricity prices and the hidden – and often unpredictable – relations with its influencing factors. In our research, we performed different experiments to predicate hourly Hungarian electricity prices using deep neural networks, for short-term and long-term, too. During this work, investigations were made to compare the results of different network structures and to determine the effect of some environmental factors (meteorologic data and date/time - beside the historical electricity prices). Our results were promising, mostly for short-term forecasts - especially by using a deep neural network with one ConvLSTM encoder.

Probability density function forecasting of electricity price: Deep gabor convolutional mixture network

This paper establishes a framework for probabilistic electricity price forecasting in the electrical markets using an end-to-end and direct approach. In the proposed method, firstly, a deep Gabor filter-oriented convolutional network is designed as the strong deep structure. Then, the designed deep Gabor network is developed as a deep mixture network to predict a set of probability density functions (PDFs) in the next hours. To do so, a probabilistic loss function is formulated. The results are validated by the actual electricity market data in California independent system operator (CAISO) and show high level of accuracy and reliability in comparison with other state-of-the-arts methods

A hybrid day-ahead electricity price forecasting framework based on time series

Electricity price forecasting (EPF) plays an indispensable role in the decision-making processes of electricity market participants. However, the complexity of electricity markets has made EPF increasingly difficult. Currently, popular methods for EPF are based on signal decomposition and suffer from computational redundancy and hyperparameter optimization challenges. In this paper, we propose a new hybrid forecasting framework to improve the forecasting accuracy of day-ahead electricity prices. The proposed model consists of three valuable strategies. First, an adaptive copula-based feature selection (ACBFS) algorithm based on the maximum correlation minimum redundancy criterion is proposed for selecting model input features. Second, a new method of signal decomposition technique for EPF field is proposed based on decomposition denoising strategy. Third, a Bayesian optimization and hyperband (BOHB) optimized long short-term memory (LSTM) model is used to improve the effect of hyperparameter settings on the prediction results. The effectiveness of the different techniques was broadly cross-validated using five datasets set up for the PJM electricity market, and the results indicated that the proposed hybrid algorithm is more effective and practical for day-ahead EPF.

Short-Term Electricity Price Forecasting Based on the Two-Layer VMD Decomposition Technique and SSA-LSTM

Accurate electricity price forecasting (EPF) can provide a necessary basis for market decision making by power market participants to reduce the operating cost of the power system and ensure the system’s stable operation. To address the characteristics of high frequency, strong nonlinearity, and high volatility of electricity prices, this paper proposes a short-term electricity price forecasting model based on a two-layer variational modal decomposition (VMD) technique, using the sparrow search algorithm (SSA) to optimize the long and short-term memory network (LSTM). The original electricity price sequence is decomposed into multiple modal components using VMD. Then, each piece is predicted separately using an SSA-optimized LSTM. For the element with the worst prediction accuracy, IMF-worst is decomposed for a second time using VMD to explore the price characteristics further. Finally, the prediction results of each modal component are reconstructed to obtain the final prediction results. To verify the validity and accuracy of the proposed model, this paper uses data from three electricity markets, Australia, Spain, and France, for validation analysis. The experimental results show that the proposed model has MAPE of 0.39%, 1.58%, and 0.95%, RMSE of 0.25, 0.9, and 0.3, and MAE of 0.19, 0.68, and 0.31 in three different cases, indicating that the proposed model can well handle the nonlinear and non-stationarity characteristics of the electricity price series and has superior forecasting performance.

JEPX spot prices forecasting system using GIS

Japan Electric Power Exchange (JEPX) is the only electricity market in Japan that allows transactions for electric power established in 2005. Several commodities are traded there, such as the spot market and the forward market. In particular, the spot market is a major trading market. The price of the spot market changes depending on the relationship between supply and demand. Therefore, it is important to forecast spot market prices to make a supply and demand plan for the next day. This research focused on the factors that determine supply and demand are related to geospatial information—first, deriving the explanatory variables for JEPX spot prices using Geographic Information System (GIS). Then, constructing the spot price forecasting system by machine learning using the derived explanatory variables. By using this system, it is possible to forecast electricity prices with higher accuracy than existing methods.

An Online Electricity Market Price Forecasting Method Via Random Forest

Electricity price forecasting (EPF) is essential to the bidding strategy formulation and market operation. Since EPF is important in the electricity market, lots of forecasting approaches are proposed. However, the new scene caused by the volatility of renewable power generation and other volatility factors has made previous methods inaccurate and inapplicable. To address this problem, we propose an online self-adaptive forecasting method based on random forest, which is different from the traditional batch learning. Our approach takes possible fluctuations of the market into consideration, and adapts to them by maintaining training sets of different sizes. A case study using actual electricity market data has shown that our proposed approach obtains higher accuracy than ordinary approaches, as well as sheds light on possible concept drift in the market.

Joint optimization of preventive and condition-based maintenance for offshore wind farms

High costs of maintenance and lost production during downtime are a challenge to the offshore wind industry, and there is a great potential to improve cost efficiency by improved maintenance and control strategies utilizing condition monitoring information. As wind farms get older, there is also an increased need to find ways of extending the lifetime of wind turbines allowing continued operation. This may be obtained by de-rating strategies, meaning adjustments of the power production to reduce the fatigue loads on the turbines. This subsequently means wind farm operators are faced with a trade-off between maximizing power production while limiting the degradation of the turbines. To investigate the best trade-off, this paper presents an optimization framework that considers component condition and planned power production to find the best times to perform predetermined preventive and condition-based maintenance on an offshore wind farm. To solve the scheduling problem, it is formulated as a constrained integer linear program, maximizing the net income for the planning horizon. The proposed method considers logistic restrictions, wind and electricity price forecasts, control strategies, component condition and probability of failure. Moreover, the method uses a short time horizon (days) to utilise weather forecasts and a long time horizon (weeks) to better capture the impact of deteriorating condition. The model is presented in a general framework for accounting for component condition in offshore wind farm operation and maintenance. It is illustrated for a specific potential application, considering condition monitoring of main bearings and corrosion of structural elements as examples.

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%.

A pattern classification methodology for interval forecasts of short-term electricity prices based on hybrid deep neural networks: A comparative analysis

Precisely identifying multidimensional trends and hidden characteristics that relate to short-term electricity price fluctuations and providing reliable predictions of future trends are difficult tasks. Instead of utilizing conventional regression-based forecasting methods, we present a novel methodology to address the problem of obtaining reliable forecasts from a pattern classification standpoint. Given that an attention mechanism is better able to capture global characteristics, a multi-head self-attention (MHSA) mechanism is adopted to extract features at long time scales more efficiently and ensure that long-term dependencies can be captured. On this basis, a new hybrid framework composed of nested long short-term memory (NLSTM), the MHSA mechanism combined with a convolutional neural network (MHSAC), and a feature space identification approach is established for robust interval forecasts. To verify the performance of our framework and demonstrate its application potential, the proposed classifier is compared to benchmarks under various scenarios (8 different input dimensions and 25 different input sizes) in terms of different performance criteria. The results indicate that our framework can be used as a valid alternative for electricity price forecasting, and it achieves satisfactory forecasting accuracy with different input dimensions. Furthermore, compared with regression-based models and price spike forecasting cases, our framework is more effective than the benchmarks. The findings also suggest that appropriate feature selection is more conducive to improving model forecasting accuracy than blindly increasing the dimensions of the input data, and the proposed framework that incorporates the MHSA mechanism is also propitious for further improving the efficiency of electricity price forecasting.

Wholesale Electricity Price Forecasting Using Integrated Long-Term Recurrent Convolutional Network Model

Electricity price forecasts have become a fundamental factor affecting the decision-making of all market participants. Extreme price volatility has forced market participants to hedge against volume risks and price movements. Hence, getting an accurate price forecast from a few hours to a few days ahead is very important and very challenging due to various factors. This paper proposes an integrated long-term recurrent convolutional network (ILRCN) model to predict electricity prices considering the majority of contributing attributes to the market price as input. The proposed ILRCN model combines the functionalities of a convolutional neural network and long short-term memory (LSTM) algorithm along with the proposed novel conditional error correction term. The combined ILRCN model can identify the linear and nonlinear behavior within the input data. ERCOT wholesale market price data along with load profile, temperature, and other factors for the Houston region have been used to illustrate the proposed model. The performance of the proposed ILRCN electricity price forecasting model is verified using performance/evaluation metrics like mean absolute error and accuracy. Case studies reveal that the proposed ILRCN model shows the highest accuracy and efficiency in electricity price forecasting as compared to the support vector machine (SVM) model, fully connected neural network model, LSTM model, and the traditional LRCN model without the conditional error correction stage.

Day Ahead Electricity Price Forecasting Based on the Deep Belief Network

With the reform of electric power system, major progress has been made in the construction of the electricity market. Electricity prices are a key influencing factor in the electricity market, and each participant trades electricity based on the price of electricity. Therefore, improving the accuracy of electricity price forecasts is important for every player in the electricity market. Prediction using single-layer neural networks has limited accuracy. Due to the high accuracy of machine learning in forecasting, the method of deep belief network is used to predict the price of electricity in the future. Real data from the U.S. PJM electricity market are used for simulation and compared with the prediction models of other neural networks. The results show that the prediction accuracy of the deep belief network model is higher, and the use of the deep belief network can provide an effective method for China’s electricity sales companies to predict electricity prices.