Power Load Prediction Research Articles

A Hybrid Double Forecasting System of Short Term Power Load Based on Swarm Intelligence and Nonlinear Integration Mechanism

Accurate and reliable power load forecasting not only takes an important place in management and steady running of smart grid, but also has environmental benefits and economic dividends. Accurate load point forecasting can provide a guarantee for the daily operation of the power grid, and effective interval forecasting can further quantify the uncertainty of power load on this basis to provide dependable and precise load information. However, most of the previous work focuses on the deterministic point prediction of power load and rarely considers the interval prediction of power load, which makes the prediction of power load not comprehensive. In this study, a new double hybrid load forecasting system including point forecasting module and interval forecasting module is developed, which can make up for the shortcomings of incomplete analysis for the existing research. The point forecasting module adopts a nonlinear integration mechanism based on Back Propagation (BP) network optimized by Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) to improve the accuracy of point prediction. A fuzzy clustering interval prediction method based on different data feature classification is successfully proposed which provides an effective tool for load uncertainty analysis. The experiment results show that the system not only has a good effect in accurately predicting power load, but also can analyze the uncertainty of the power load, which can be used as an effective technology of power system planning.

Empirical Mode Decomposition based Multi-objective Deep Belief Network for short-term power load forecasting

With the rapid development of power grid data, the data generated by the operation of the power system is increasingly complex, and the amount of data increases exponentially. In order to fully exploit and utilize the deep relationship between data to achieve accurate prediction of power load, this paper proposes an Empirical Mode Decomposition Based Multi-objective Deep Belief Network prediction method (EMD-MODBN). In the training process of DBN, a multi-objective optimization model is constructed aiming at accuracy and diversity, and MOEA/D is used to optimize the parameters of the model to enhance the generalization ability of the prediction model. Finally, the final load forecasting results are obtained by summing up the weighted outputs of each forecasting model with ensemble learning method. The experimental results show that compared with several current better load forecasting methods, this method has obvious advantages in prediction accuracy and generalization ability.

Power Load Demand Forecasting Model and Method Based on Multi-Energy Coupling

At the present stage, China’s energy development has the following characteristics: continuous development of new energy technology, continuous expansion of comprehensive energy system scale, and wide application of multi-energy coupling technology. Under the new situation, the accurate prediction of power load is the key to alleviate the problem that the planning and dispatching of the current power system is more complex and more demanding than the traditional power system. Therefore, firstly, this paper designs the calculation method of the power load demand of the grid under the multi-energy coupling mode, aiming at the important role of the grid in the power dispatching in the comprehensive energy system. This load calculation method for regional power grid operating load forecasting is proposed for the first time, which takes the total regional load demand and multi-energy coupling into consideration. Then, according to the participants and typical models in the multi-energy coupling mode, the key factors affecting the load in the multi-energy coupling mode are analyzed. At this stage, we fully consider the supply side resources and the demand side resources, innovatively extract the energy system structure characteristics under the condition of multi-energy coupling technology, and design a key factor index system for this mode. Finally, a least squares support vector machine optimized by the minimal redundancy maximal relevance model and the adaptive fireworks algorithm (mRMR-AFWA-LSSVM) is proposed, to carry out load forecasting for multi-energy coupling scenarios. Aiming at the complexity energy system analysis and prediction accuracy improvement of multi-energy coupling scenarios, this method applies minimal redundancy maximal relevance model to the selection of key factors in scenario analysis. It is also the first time that adaptive fireworks algorithm is applied to the optimization of adaptive fireworks algorithm, and the results show that the model optimization effect is good. In the case of A region quarterly load forecasting in southwest China, the average absolute percentage error of a least squares support vector machine optimized by the minimal redundancy maximal relevance model and the adaptive fireworks algorithm (mRMR-AFWA-LSSVM) is 2.08%, which means that this model has a high forecasting accuracy.

Multi-load prediction modelling for combined energy system of intelligent city buildings

Aiming at the problem that there is a big difference between the predicted load and the actual load, a multi-load forecasting model for the combined energy system of intelligent city buildings is proposed. Using DeST to construct the building model and simulate the load characteristics of the building, the composite model of the building is constructed and the structure of the energy system is defined. Based on Bayesian theory, the multi-load model of building energy system is constructed. MCMCMC algorithm is used to optimise the multi-load model of building composite energy system, realise multi-load prediction and promote the development of energy industry. The experimental results show that the power load prediction and heat load prediction of this method are always higher than those of other methods, and MAPE is lower than 0.2, which proves that this method can accurately predict the multi-load of buildings with high prediction accuracy.

Multi-load prediction modelling for combined energy system of intelligent city buildings

Aiming at the problem that there is a big difference between the predicted load and the actual load, a multi-load forecasting model for the combined energy system of intelligent city buildings is proposed. Using DeST to construct the building model and simulate the load characteristics of the building, the composite model of the building is constructed and the structure of the energy system is defined. Based on Bayesian theory, the multi-load model of building energy system is constructed. MCMCMC algorithm is used to optimise the multi-load model of building composite energy system, realise multi-load prediction and promote the development of energy industry. The experimental results show that the power load prediction and heat load prediction of this method are always higher than those of other methods, and MAPE is lower than 0.2, which proves that this method can accurately predict the multi-load of buildings with high prediction accuracy.

Power load prediction method based on kernel extreme learning machine with t-distribution variation bat algorithm

To improve accuracy of power system load forecasting, according to nonlinearity and uncertainty of load sequence, a power load forecasting method combined with kernel extreme learning machine (KELM) and adaptive variation bat optimization algorithm (AMBA) is proposed in this paper. The model determines mutation probability of the current optimal individual according to the population fitness variance and the value of the current optimal solution and the t-distribution variation is performed on the global optimal individual, and bat individual after mutation is subjected to secondary optimization. Then AMBA is used to optimize the network parameters of kernel function extreme learning machine, so that make full use of advantages of fast learning speed and generalization ability of KELM to realize rapid prediction of load. AMBA-KLEM model is used to predict load of Ningxia Power Grid, and the results are compared with ELM model and KELM model. The results show that the model has higher load prediction accuracy.

Power Load Forecasting using Back Propagation Algorithm

The day today operation and scheduling activities of a power generation unit requires the forecast of power demand to meet the needs of the consumers. Load forecasting is categorized into short, medium and long term forecasts. The short term prediction refers to hourly load forecast for the period lasting from an hour to more than a few days. The medium term forecasts is prediction of power load ranging from one to several months ahead. Finally, the long term forecast indicates prediction of power load ranging from one to many years in near future. Excellence of short term hourly power load forecast has a considerable effect on cost effective functioning of power plants because numerous assessments are dependent on these forecasts. These assessments comprise cost effective scheduling of power plants, scheduling of coal acquisition, power plant security consideration, and planning for energy related business. The significance of precise load forecasts will reduce the wastage of power in the future because of remarkable changes taking place in the structure of power generation industries due to deregulation and heavy competition. This situation creates thrust on the power generation sectors to function at maximum possible efficiency, which leads to precise forecasting of the power load.

Short-term power load forecasting based on Elman neural network with particle swarm optimization

The prediction of short term power load owns a great influence on performance of the whole electric system. Raising the result of power load forecasting is always a research spot. This paper proposes a method combined Elman neural network (ENN) and the particle swarm optimization (PSO) for the short-term power load forecasting. Firstly, this paper introduces the principle of ENN and PSO algorithm respectively and analyzes performance of network influenced by parameters of ENN. Then the particle swarm optimization algorithm is applied to searching the optimal learning rate of ENN. To study the capability of the method proposed in this paper, the method is utilized for the short-term power load forecasting, which is suitable to be solved by ENN. Besides, a comparison experiment on this method (PSO–ENN) with general regression neural network (GRNN), the original ENN and the traditional back-propagation neural network (BPNN) is given out to illustrate effectiveness of PSO–ENN.

An Effective Artificial Neural Network based Power Load Prediction Algorithm

An Effective Artificial Neural Network based Power Load Prediction Algorithm

Climate Change and Power Security: Power Load Prediction for Rural Electrical Microgrids Using Long Short Term Memory and Artificial Neural Networks

Many of rural Alaskan communities operate their own, stand-alone electrical microgrids as there is no state-wide power distribution network. Although the fossil fuel-based power generators are the primary energy source in these isolated communities, an increasing number of microgrids have started to diversify their power supply by including renewable wind, hydro and photovoltaic energy sources. The integration and optimization of the multiple energy sources requires a design of a new power management system that can anticipate how much electricity will be drawn by the community and how much electricity will be generated from the renewable sources in order to control the fossil fuel-based generators to meet the community power demand. To address this problem, we designed a hybrid machine learning algorithm to predict community power draw as one module for the next generation microgrid power management system. The algorithm divides the task of a power load prediction into two sub-models: the first temporal model predicts the future weather conditions and the second model is trained to associate the predicted weather conditions with the community power demand. The results illustrate (1) the feasibility of building a machine learning algorithm that uses relatively small data for model training and validation, (2) the ability to predict the near-future community power load for the microgrids operating in the environments with highly dynamic weather conditions and (3) how the integration of multiple low quality future weather conditions produced high accuracy power load prediction.

A Short-Term Power Load Prediction Algorithm of Based on Power Load Factor Deep Cluster Neural Network

Accurate short-term prediction of power system load is of great importance to improvement of power system’s stability and electrical equipment’s safety, and a critical step for power load prediction is clustering of existing historical information of load. Since load itself is a time series data with high dimensionality, and meanwhile, load is influenced by meteorological factors and seasonal factors, as a result it’s difficult to establish simple linear relationships with the load and these factors, and consequently, the clustering quality obtained by traditional algorithms is low, which further affects quality of load prediction. Deep learning algorithm was introduced in this paper to construct a power load factor deep cluster neural network (PLDCNN) which consisted of multi-group neural network. Station operating data for consecutive 3 years were introduced in the simulation experiment. PLDCNN was compared with traditional clustering algorithms, and experiment indicated that PLDCNN could describe and classify power load information more accurately, and short-term load prediction of electric power system based on PLDCNN could also archive higher accuracy.

The prediction of the impact of climatic factors on short-term electric power load based on the big data of smart city

The climate changes have great impact on the residents’ electricity consumption, so the study on the impact of climatic factors on electric power load is of significance. In this paper, the effects of the data of temperature, rainfall and wind of smart city on short-term power load is studied to predict power load. The authors studied the relation between power load and daily temperature, rainfall and wind in the 31 days of January of one year. In the research, the authors used the Matlab neural network toolbox to establish the combinational forecasting model. The authors trained the original input data continuously to get the internal rules inside the data and used the rules to predict the daily power load in the next January. The prediction method relies on the accuracy of weather forecasting. If the weather forecasting is different from the actual weather, we need to correct the climatic factors to ensure accurate prediction.

Study on the Cooling Heating and Power Load Prediction Method in Community Building Energy Planning

Community building load prediction is the foundation and key to community building energy planning and community energy supply system design. The present load prediction methods for a single building are relatively mature. But load prediction for community buildings are more complicated than that for single buildings. In this paper, load prediction methods currently used for community buildings are first discussed categorically. Based on the previous work, prototypical building scenario load prediction method is proposed. Then taking Dawangdian community as an example, prototypical building scenario load prediction method is used to predict the cooling, heating and power loads.

Short-term power load probability density forecasting method using kernel-based support vector quantile regression and Copula theory

Penetration of smart grid prominently increases the complexity and uncertainty in scheduling and operation of power systems. Probability density forecasting methods can effectively quantify the uncertainty of power load forecasting. The paper proposes a short-term power load probability density forecasting method using kernel-based support vector quantile regression (KSVQR) and Copula theory. As the kernel function can influence the prediction performance, three kernel functions are compared in this work to select the best one for the learning target. The paper evaluates the accuracy of the prediction intervals considering two criteria, prediction interval coverage probability (PICP) and prediction interval normalized average width (PINAW). Considering uncertainty factors and the correlation of explanatory variables for power load prediction accuracy are of great importance. A probability density forecasting method based on Copula theory is proposed in order to achieve the relational diagram of electrical load and real-time price. The electrical load forecast accuracy of the proposed method is assessed by means of real datasets from Singapore. The simulation results show that the proposed method has great potential for power load forecasting by selecting appropriate kernel function for KSVQR model.

Adaptive Power Efficiency Control by Computer Power Consumption Prediction Using Performance Counters

As the number of data centers has grown in pace with the burgeoning expansion in information technology (IT), reducing data center power consumption has become an important social challenge. Since IT equipment is a primary contributor to the data center power consumption, it is also an area where power conservation efforts are indispensable. Nevertheless, although the efficiency of the power supply modules integrated into computers has recently seen significant improvements, their overall efficiency primarily depends on load rates. This is particularly true under low-power load conditions, where efficiency is known to decrease drastically. Recently, power-saving techniques that work by controlling the power module configuration under low-power load conditions have been considered, and using such techniques, it is expected that further efficiency improvements can be achieved by interlocking the computer load status with the power supply configuration control in real time. In this paper, the performance counters built into computer processors are used to predict power load variations, and an equation that facilitates the prediction of server power consumption levels is defined. In a server application experiment utilizing prototype computer hardware and multiple regression analysis, we confirmed that the power prediction equation could precisely predict computer power requirements and achieve adaptive efficiency control. In order to further validate the efficiency control effect, a circuit simulation was conducted. The simulation results showed that a power loss reduction of up to 28.3% could be achieved using the model equations to control the power supply module efficiency.

Short term power load prediction with knowledge transfer

A novel transfer learning method is proposed in this paper to solve the power load forecast problems in the smart grid. Prediction errors of the target tasks can be greatly reduced by utilizing the knowledge transferred from the source tasks. In this work, a source task selection algorithm is developed and the transfer learning model based on Gaussian process is constructed. Negative knowledge transfers are avoided compared with the previous works, and therefore the prediction accuracies are greatly improved. In addition, a fast inference algorithm is developed to accelerate the prediction steps. The results of the experiments with real world data are illustrated.

Power Load Prediction Based on Fractal Theory

The basic theories of load forecasting on the power system are summarized. Fractal theory, which is a new algorithm applied to load forecasting, is introduced. Based on the fractal dimension and fractal interpolation function theories, the correlation algorithms are applied to the model of short-term load forecasting. According to the process of load forecasting, the steps of every process are designed, including load data preprocessing, similar day selecting, short-term load forecasting, and load curve drawing. The attractor is obtained using an improved deterministic algorithm based on the fractal interpolation function, a day’s load is predicted by three days’ historical loads, the maximum relative error is within 3.7%, and the average relative error is within 1.6%. The experimental result shows the accuracy of this prediction method, which has a certain application reference value in the field of short-term load prediction.

The Power Load Prediction Based on Improved Genetic Neural Network

There exist nonlinear and high redundancy between the power load factors, and the traditional methods in neural network can not eliminate the redundancy in the prediction data and capture the nonlinear characteristics, resulting in lower prediction precision of the power load. In order to improve the prediction precision of the power load, a power load prediction method based on improved genetic algorithm optimization neural network (IGA-BP) is put forward. First, the power load is reconstructed using the correlation function, and then performed the normalization processing. Secondly, the power load training sample is input into the BP neural network for learning, and then the initial connection weights and thresholds of the BP neural network are selected using the improved genetic algorithm. Finally, the power load prediction model is established. Simulation results show that the improved neural network can reflect the trend of complex non-linear power load, achieve higher power load fitting and prediction precision, and is an effective load prediction method for power system. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4302

The Predictive Method of Power Load Based on SVM

For the problems which exist in power load prediction, such as nonlinear and high dimension recognition, support vector machine (SVM) is applied to power load prediction technology, based on the theory analysis of SVM and power load prediction. The predictive model of power load is established by SVM, which overcomes the problems to a great extent such as dimension disaster, over-learning, etc. The new thought of power load prediction is proposed which is more accurate, more intelligent and more humanized. The historical load data from a power grid company are used to establish the predictive model, Simulation is done by Matlab, and the predictive precision is improved greatly. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4834

Short-Term Power Load Forecasting Based on the Combination of Improved Neural Network Model and Fuzzy Decision Model

A fuzzy decision model is built to analyze correlation of historical load datas, and to preprocess original datas, by extracting useful datas contributing to forecasting and removing "bad datas". Then the neural network model (NNM) is established to predict power load value measured at 96 time points, through accurate analysis, the rationality of the model is verified.