Power Load Forecasting Research Articles

Design of Distributed Electricity Trading Mechanism Based on Contract for Difference

A contract for difference is a medium and long-term financial contract, which can be used in the electricity market to lock the electricity price in advance and avoid the risk of electricity price fluctuations in the spot market. The construction of the domestic power spot market has just started. With the release of relevant policies and the gradual improvement of the market structure, it is urgent to design a corresponding trading mechanism to ensure the smooth transition of the market. The current day-ahead transactions, real-time transactions and other short-term transactions for distributed power generation, on the one hand power load forecasting, electricity price demand response and other related technologies need to be further improved, on the other hand due to the randomness and uncertainty of distributed energy, participating in the short-term spot market has large price fluctuations, which is not conducive to the stability of the electricity market, and it is also not conducive to the consumption of distributed energy. Aiming at the above problems, this paper uses the characteristics of CFDs to restrain market power to design a distributed energy trading mechanism to achieve the purpose of energy saving and emission reduction.

Short-term power load forecasting based on I-GWO-KELM algorithm

In this paper, I-GWO-KELM algorithm is used for short-term power load forecasting. Normalize the power data and meteorological data of the short-term power load, and use GWO to optimize the regularization coefficient of KELM and the RBF kernel parameters. To apply the model to short-term power load forecasting to obtain simulations for the next 24 hours and 168 hours curve. Experiments show that the improved model I3-GWO-KELM proposed in this paper has the best effect. The improvement of GWO in this paper is effective and feasible. In the application of short-term power load forecasting, the IGWO-KELM model is more accurate than the ELM and KELM models.

Long-term load combination forecasting method considering the periodicity and trend of data

In order to solve the problems of insufficient accuracy of long-term power load forecasting and poor applicability of the model, this paper considers the coupling of a number of macro indicators, such as regional economic development and social development indicators, with the time series data of regional power load. BP neural network and Autoregressive integrated moving average model (ARIMA) are used to integrate and improve the forecasting model, so as to improve the trend forecasting ability of annual load forecasting model. The non parametric function method is used to forecast the periodic load data in the monthly load data, the annual load forecast is combined with the monthly load forecast to improve the overall forecasting accuracy of the model. Finally, through the comparison of grey prediction and other models and the verification of MAPE error analysis method, the prediction accuracy of the model method considering the combination of data periodicity and trend is significantly improved, which is suitable for the long-term prediction of regional power load.

A centralized monitoring interface for microgrid using lab view

Micro-grids care the electric utility main grid through allowing the integration of emergent distributions of different energy resources. In addition, distributed energy resources (DERs) decrease energy losses and improving the efficiency of the electric power system. The DERs integrated micro grid structure will involuntarily require perfect, specific, as well as reactive power control to make sure well-organized power flow. It is important to cultivate a back-end system (user interface) to permit simple access to the whole power system development. This paper proposes a Lab VIEW based user-friendly front-end and back-end interfaces for PV system integration in micro grid. The proposed interface permits a control programming in the block end which offers the opportunities for deploying the comprehensive and inclusive control strategies. The system consists of load scheduling and PV generation during load dispatch epoch. The proposed LabVIEW control system also manage to pay for monitoring and tracking of maximum PV power point selections which allow insightful PV analysis forecast to establish the subsequent future exploit. The proposed PV interface used artificial neural network back propagation for the PV power Load Forecasting.

Short Term Load Forecasting Based on SBiGRU and CEEMDAN-SBiGRU Combined Model

With the continuous development of global science and technology industry, the demand for power is increasing, so short-term power load forecasting is particularly important. At present, a large number of load forecasting models have been applied to short-term load forecasting, but most of them ignore the error accumulation in the iterative training process. To solve this problem, this article proposes a combined measurement model which combines stacked bidirectional gated recurrent unit (SBiGRU), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and error correction. In the first stage, SBiGRU model is established to study the time series characteristics of load series under the influence of temperature and holiday types. The error series generated in the prediction process of SBiGRU model reflects the error characteristics of load series; In the second stage, the error sequence is decomposed into several intrinsic mode functions (IMF) components and trend components by CEEMDAN algorithm. The SBiGRU model is established again for each component to learn and predict, and the predicted values of all components are reconstructed to get the error prediction results; Finally, sum the two-stage prediction results to correct the error. The accuracy of SBiGRU-CEEMDAN-SBiGRU combination model is evaluated by two public power load data. The experimental results show that the SBiGRU-CEEMDAN-SBiGRU combination model has better accuracy and stability than the traditional model.

Research on Short-Term Forecast of Power Load Based on Adam-Gru

To forecast the short-term power load accurately and reliably, a short-term power load model based on gate recurrent unit was proposed. In order to improve the accuracy of the model, the Adam algorithm is used to calculate the GRU hyperparameter Optimization, so as to construct an Adam-GRU short-term power load forecasting model. The performance of the forecasting model was evaluated by the Root Mean Square Error and running time, then the forecast results were compared with LSTM method. The experimental results show that Adam-GRU has a higher accuracy than the LSTM model, an increase of 15.54%, and a time complexity reduction of 15.75%. Compared with the RNN model, the accuracy has improved 10.84%. The results show that the short-term power load forecasting model and parameter optimization method based on Adam-GRU can effectively forecast the power load data.

A short-term load forecasting model of multi-scale CNN-LSTM hybrid neural network considering the real-time electricity price

With the innovation of power market and the development of energy intelligent technology, load forecasting technology as an important direction of power system development plays an important role in power system planning. Aiming at the problem of insufficient feature extraction and low prediction accuracy, a short-term load forecasting model of multi-scale CNN-LSTM hybrid neural network considering the real-time electricity price is proposed in this paper. Firstly, the maximum information coefficient method is used to analyze the correlation between electricity price and load. The historical load, real-time electricity price, weather and other factors are constructed in the form of continuous feature maps as input. Secondly, the Convolutional Neural Network (CNN) is used to cascade the shallower and deeper feature from four different scales. Feature vectors of different scales are fused as the input of Long Short Term Memory (LSTM) network , and LSTM network is used for short-term load forecasting. Finally, the proposed method is used to predict the real load data of a city in Liaoning Province. The experimental results show that the proposed method has higher prediction accuracy than the standard LSTM model, Support Vector Machine (SVM) model, Random Forest (RF) model and Auto Regressive Integrated Moving Average (ARIMA) model. Besides, the prediction results show that this study has high application value and provides a new way for the development of power load forecasting.

ELECTRICITY PRICE FORECASTING MODELS

New era has begun two decades ago in the most power sectors of the globe. As electricity itself shifted from being a commodity only to a competitive trading market instrument, forecasting of power load and prices started to be another important factor. With the introduction of liberal power markets, independent players like producers, operators, traders became an important factors of liberalized power markets. New era in this secotr also increased competition among companies and countries. Thus, as new players came into scene, electricity became to be a tradable product and similar to another commodities needs to account, forecast plan of electiricity brought new challenges. With its obstacles, new atmosphere of electiricity repormation brought benefits such as lower price to end users and more utilized energy systems among the countries. Different forecasting models have been developed in order to forsee future operations. Moreover, popular modelts from economics, such as game theories, cournot model, Bertrand model, nash equilibrium plays a big role in electricity price forecasting. Simulation modelts are another methods heavily used by producers, operators and power traders in market. Additionally, statistical models, such as moving averages, Root Mean Square Error, Mean Absolute Error, mean Absolute Percentage Error, Theil's inequality coefficient are frequesntly used to determine price movements of electiricity. As its broadly used in several different industries, Time Series models using historical information and adding updated information helps to model future movement of prices. This article discusses the several methods of price forecasting of electricity in liberal power markets as high volatility of the market raises a big risk for all participants. Forecasting models explains time series analysis and briefly discusses about autoregressive, moving average, autoregressive moving average, and seasonal autoregressive moving average models. Moreover, the article illustrates examples from electricity price and load forecasts and their comparisons with actual results from trades in Turkish electricity market.

Intelligent Systems for Power Load Forecasting: A Study Review

The study of power load forecasting is gaining greater significance nowadays, particularly with the use and integration of renewable power sources and external power stations. Power forecasting is an important task in the planning, control, and operation of utility power systems. In addition, load forecasting (LF) aims to estimate the power or energy needed to meet the required power or energy to supply the specific load. In this article, we introduce, review and compare different power load forecasting techniques. Our goal is to help in the process of explaining the problem of power load forecasting via brief descriptions of the proposed methods applied in the last decade. The study reviews previous research that deals with the design of intelligent systems for power forecasting using various methods. The methods are organized into five groups—Artificial Neural Network (ANN), Support Vector Regression, Decision Tree (DT), Linear Regression (LR), and Fuzzy Sets (FS). This way, the review provides a clear concept of power load forecasting for the purposes of future research and study.

WITHDRAWN: Ultra-short-term forecast of power load based on load characteristics and embedded system

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn at the request of the Editor in Chief. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Sundhararajan Mahalingam, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal's standard workflow.

Short-term load forecasting based on LSTM-RF-SVM combined model

Aiming at the problem of low accuracy of single model forecasting power load, a combined LSTM-RF-SVM prediction model based on Long Short-Term Memory network (LSTM), Random Forest (RF) and Support Vector Machine (SVM) is designed. Firstly, the preprocessed load data, meteorological data and date data are input into the three models according to the sliding time window. The prediction results of each model are obtained through training. Then the optimal weighted combination method is uesd to calculate the weight coefficients of the three models in the prediction results. Finally, the final prediction results are obtained by integrating the weight coefficients. Experimental results show that compared with other methods, the established forecasting model improves the accuracy of short-term load forecasting.

Short-term load forecasting model based on EEMD-LSTS-ARIMA

To improve the accuracy of the short-term power load forecasting, this paper designed a model, namely, the first to use based on empirical mode decomposition (EMD) method based on the collection of ensemble empirical mode decomposition (EEMD) in order to improve signal decomposition in the process of modal aliasing phenomenon and false component, and using the decomposition method of power load data decomposition, modal components with different characteristics, and according to its characteristics can be divided into high frequency components, intermediate frequency and low frequency components, Then the Long Short-Term Memory network (LSTM) is used for load forecasting. Using the autoregressive Integrated moving average model (Arima) to correct the predicted residual error, the load forecasting model based on EEMD-LSTM-ARIMA is established. Finally, the data of Queensland, Australia is used as the research sample. On the above prediction model, the data of this region is input into the model for power load prediction. At the end of the prediction, the final prediction results are compared with the EEMD-LSTM model through root mean square error (RMSE) and mean absolute percentage error (MAPE). Through comparison, it is found that the method used in this paper has a high prediction accuracy.

Alice: A LSTM Neural Network Based Short-Term Power Load Forecasting Approach in Distributed Cloud-edge Environment

Load forecasting, as the baseline for decision-making, plays a key role in the management and control of the grid. Nevertheless, the rapid evolution of the smart grid has brought a dramatic increase in the volume of user-side data, traditional load forecasting approaches have to face the challenge of ensuring the accuracy of dynamic forecasting under the circumstance of the widespread application of big data. Meanwhile, the advance of the Industrial Internet of Things (IIoT) enables smart meters to acquire more plentiful data, which improves the accuracy of short-term load forecasting with appropriate utilization, but the gradual increase in the magnitude of data brought by IIoT technique has also left the computing equipment under great pressure. To address these challenges, in this paper, a Long Short-Term Memory(LSTM) network based short-term load forecasting approach deployed in the distributed cloud-edge environment, named Alice, is devised to deliver more precise results for the smart forecasting of power load. It adopts the LSTM network to perform the forecasting tasks and extends the whole system to the cloud-edge platform to implement parallel neural computing. Moreover, the Toeplitz Inverse Covariance-Based Clustering (TICC) algorithm is invited to enhance the efficiency of LSTM. Eventually, experimental evaluations on real dataset elaborate the superiority of the proposed Alice approach when compared to other state-of-the-art approaches.

A hybrid load forecasting model based on support vector machine with intelligent methods for feature selection and parameter optimization

Accurate power load forecasting contributes to guaranteeing safe dispatch and stable operation of a power system. As a great forecasting tool, support vector machine is widely used in power load forecasting. However, due to the rapid development of information technology, the prediction result of simple support vector machine is no longer accurate enough to forecast in the smart grid. To enhance the prediction accuracy, this paper makes some improvements on support vector machine, and proposes a hybrid model integrated with intelligent methods for feature selection and parameter optimization. Firstly, real-time price becomes an important influencing factor of power load as people increasingly rely on demand and real-time price to adjust their electricity consumption patterns. Thus, real-time price, together with other factors that affect power load, is taken as a candidate feature, and minimal redundancy maximal relevance is applied to derive informative features from candidate features. Secondly, as for another feature, the historical load sequence, to make its selection more general, this paper employs the weighted gray relation projection algorithm for holidays to be predicted. Finally, second-order oscillation and repulsion particle swarm optimization is used for optimizing parameters of support vector machine. Moreover, the proposed model is tested via simulations on datasets of Singapore. By comparing prediction results of the proposed model, the support vector machine before improvement and other three forecasting models, this paper confirms that the improvements on support vector machine are effective, and the proposed model outperforms the other forecasting models in aspect of accuracy, stability and effectiveness.

Improving Load Forecasting of Electric Vehicle Charging Stations Through Missing Data Imputation

As the penetration of electric vehicles (EVs) accelerates according to eco-friendly policies, the impact of electric vehicle charging demand on a power distribution network is becoming significant for reliable power system operation. In this regard, accurate power demand or load forecasting is of great help not only for unit commitment problem considering demand response but also for long-term power system operation and planning. In this paper, we present a forecasting model of EV charging station load based on long short-term memory (LSTM). Besides, to improve the forecasting accuracy, we devise an imputation method for handling missing values in EV charging data. For the verification of the forecasting model and our imputation approach, performance comparison with several imputation techniques is conducted. The experimental results show that our imputation approach achieves significant improvements in forecasting accuracy on data with a high missing rate. In particular, compared to a strategy without applying imputation, the proposed imputation method results in reduced forecasting errors of up to 9.8%.

Deep Neural Network and Long Short-Term Memory for Electric Power Load Forecasting

Forecasting domestic and foreign power demand is crucial for planning the operation and expansion of facilities. Power demand patterns are very complex owing to energy market deregulation. Therefore, developing an appropriate power forecasting model for an electrical grid is challenging. In particular, when consumers use power irregularly, the utility cannot accurately predict short- and long-term power consumption. Utilities that experience short- and long-term power demands cannot operate power supplies reliably; in worst-case scenarios, blackouts occur. Therefore, the utility must predict the power demands by analyzing the customers’ power consumption patterns for power supply stabilization. For this, a medium- and long-term power forecasting is proposed. The electricity demand forecast was divided into medium-term and long-term load forecast for customers with different power consumption patterns. Among various deep learning methods, deep neural networks (DNNs) and long short-term memory (LSTM) were employed for the time series prediction. The DNN and LSTM performances were compared to verify the proposed model. The two models were tested, and the results were examined with the accuracies of the six most commonly used evaluation measures in the medium- and long-term electric power load forecasting. The DNN outperformed the LSTM, regardless of the customer’s power pattern.

Research on Power Load Forecast Based on Ceemdan Optimization Algorithm

With the continuous development of the power industry, power data has become more complex. However, it is difficult for common shallow neural networks to fully extract the original load data features, thus greatly limiting the load prediction accuracy. Therefore, given the advantages of CEEMDAN, a prediction model based on CEEMDAN is proposed in the paper, the advantages of which are verified by simulation analysis of measured power data. Experiments prove that the algorithm proposed in the paper has higher prediction accuracy, which effectively overcomes the EMD modal aliasing problem, and the decomposition process is more complete, so that the prediction accuracy of the subsequent prediction model is improved.

Optimal scheduling of cogeneration microgrid with wind energy and energy storage devices

Aiming at the optimal scheduling of cogeneration microgrid, a microgrid structure including wind energy, energy storage device, solar thermal power station, gas turbine and waste heat boiler is proposed. Based on the uncertainty of wind power output and load forecasting, according to beta distribution wind power output and normal distribution load forecasting, the CHP microgrid optimal scheduling model of photothermal power station is established and solved by genetic algorithm. Finally, the minimum operation cost of the system under different confidence levels is discussed. The results show that the minimum operating cost of the system can be significantly reduced by adding wind power generation into the microgrid structure, and the relationship between the confidence level of load forecasting and the minimum operating cost of the system is studied.

An intelligent power flow adjustment method based on reinforcement learning

In order to examine the future power grid operation, the operators usually build future power system model files first according to boundary conditions such as infrastructure plans, maintenance plans, power generation plans, load forecasts, and power plan requirements. As these plans are usually prepared independently, and the scale of the power grid is getting larger and larger, the newly built grid model is usually unsolved, ill, or does not meet the actual grid operation requirements. In this situation, the operators normally have to readjust the units power many times, according to their own experience, to obtain a qualified power flow result. A method for power flow adjustments is proposed in this paper. The Q-Learning (QL) method is used to adjust the power flow from nonconvergence to convergence, and then some direction guidance for subsequent adjustments is provided through certain safety and economic indicators of the power grid (such as network loss, output of the balancing machine, etc.). Finally, two examples of IEEE39 nodes system and Zhejiang power grid are presented to verify the effectiveness and practicability of the proposed method.

Study on Ultra Short Term Prediction of Residential Power Load

Because of the increasing demand of modern residents for electric energy, the power supply load of electric power enterprises also increases. Under this condition, affected by the randomness of electricity consumption behavior of different residents, the electricity load generated by each resident will fluctuate greatly, which directly leads to the decline of power safety level. Under this condition, as the power manager, the power enterprise must guarantee the operation safety of the system and scheduling is the main means to achieve the goal. However, if the dispatching work is not carried out in time, it cannot completely eliminate the impact of residential power load. At this time, modern scholars put forward a super short-term forecasting technology of residential power load based on long-term and short-term memory cyclic neural network. The implementation of this technology can provide strong support for the power dispatching work, make the dispatching work more real-time, thus avoiding the negative residential power consumption the impact of the lotus. This paper mainly studies the ultra-short term forecasting of residential power load, expounds the basic concept and model building method of this method, and verifies its effectiveness through simulation and examples.