186 results about "Energy load" patented technology

The invention discloses a multi-domain collaborative flexible load schedulable potential evaluation and energy management method, and relates to the technical field of park energy management, and the method comprises the steps: obtaining space-time multi-source data of a smart park, constructing a graph neural network power consumer clustering model based on iterative self-organization analysis, and generating a power consumption behavior portrait of a power consumer; based on power utilization parameters of building air conditioners, electric vehicles, park ponds and energy storage batteries in the smart park, the schedulable potential of the multi-element flexible resources is evaluated; and based on the hybrid neural network and the Harris eagle optimization algorithm, constructing a load prediction model, and predicting various types of energy loads in a future time period. The invention aims to establish an accurate model and method, accurately evaluate the schedulable potential of different types of flexible loads in different scenes, realize efficient utilization, energy conservation and emission reduction and optimal configuration of park energy, and improve the overall energy management level and operation efficiency of the park.

A multi-energy load forecasting method, a multi-energy load forecasting system, an electronic device, a program, and a storage medium are provided that realize accurate long-term forecasting of multi-energy loads in a target integrated energy system under conditions where no historical load data is available. [Solution] A multi-energy load forecasting method for an integrated energy system without historical data involves obtaining the meteorological characteristics of a target complex and the cooling, heating, electricity, and gas historical data of a source domain group complex, preprocessing the obtained data, performing cross-correlation and generalization ability analysis of the complex on the preprocessed cooling, heating, electricity, and gas historical data of the source domain group complex, determining appropriate source domain data, constructing a multi-energy load forecasting model, training the model based on the source domain data according to the Metas training policy, obtaining a trained forecasting model, and inputting the preprocessed meteorological characteristics of the target complex into the forecasting model to obtain a forecast result.

The invention relates to the technical field of energy load prediction distribution optimization, in particular to an energy terminal-oriented dynamic load prediction distribution optimization method and system. The method comprises the following steps: generating a load prediction value by using a long short-term memory network based on acquired terminal load data; constructing a load priority matrix and a load distribution conflict rule set, and constructing a multi-factor integration constraint model in combination with the optimization parameters of the load predicted value; dynamically correcting the model based on the load prediction value; an integer programming problem is solved, and a distribution instruction of each terminal is obtained; and performing execution control based on the distribution instruction. The distributed acquisition unit supports multiple communication modes, acquires and normalizes various types of terminal data, loads a virtual value compensation model for checking abnormal nodes, guarantees data integrity and reliability, improves the processing capability of the system for multi-source heterogeneous data, and enhances compatibility and robustness.

The invention discloses an integrated energy load prediction method and system based on modal decomposition and TCN-Transform fusion, and aims to solve the key problems of low prediction precision, insufficient utilization of meteorological factor and load correlation, insufficient optimization of a model structure and the like in integrated energy system load prediction. The method comprises the following steps: comprehensively acquiring electric load, cold load, thermal load and various meteorological data, acquiring different types of data by adopting a special device, and then preprocessing the data; using a maximum information coefficient correlation analysis method to screen remarkably related meteorological features; determining an optimal decomposition parameter in combination with variational mode decomposition and a crown porcupine optimization algorithm; a prediction model fusing TCN and Transform advantages is constructed, and the structure is optimized according to load prediction characteristics; the precision and stability of load prediction of the integrated energy system are remarkably improved, the relation between the integrated energy load and external factors is reflected more comprehensively, and a reliable load prediction basis is provided for optimized operation and management of the integrated energy system.

The invention discloses a comprehensive energy low-carbon optimization scheduling method and system based on a master-slave game. The method comprises the following steps: constructing a hydrogen-containing comprehensive energy system model, and inputting data of an external energy supply side, energy conversion and energy storage equipment and a load side to obtain initialized system operation parameters; building a user side model based on the model, generating an initial electricity / heat load demand, calculating a user alternative energy consumption rate and dynamically adjusting an electricity / heat energy conversion ratio in combination with a price difference of real-time electricity and heat prices and user energy consumption behavior characteristics, and forming a new electricity / heat load distribution scheme; the integrated energy operator generates an electricity / heat price signal and formulates a dual excitation strategy, the user aggregator adjusts the user energy load and response strategy according to the signal and the strategy and feeds back the strategy to the integrated energy operator, and the user aggregator and the integrated energy operator adjust bidirectional feedback iteration to solve Stackelberg balance through the electricity / heat price signal and the load to obtain an optimal scheduling scheme. According to the invention, economic low-carbon operation and benefit win-win between subjects can be realized.

The invention discloses a carbon emission dynamic prediction method and system based on multi-energy load data. The method comprises the following steps: collecting and preprocessing carbon emission, energy consumption and multi-energy load consumption historical data of a park, calculating a joint correlation coefficient, and screening energy types; calculating an initial carbon emission-energy conversion matrix and an energy-multi-load conversion matrix; constructing a carbon emission coupling objective function, and solving and updating a corresponding conversion matrix by alternately fixing a carbon emission-energy and energy-multi-energy load conversion matrix; establishing a carbon emission coupling model, calculating a predicted carbon emission error, constructing a predicted error correction objective function, and correcting a conversion matrix; and S4, predicting the multi-energy load consumption in the (M + 1) th month and correcting errors, and predicting the carbon emission in the (M + 1) th month in combination with the conversion matrix corrected in the step S4. According to the method, the conversion relation between the carbon emission and the multi-energy load can be dynamically adjusted, and the accuracy of carbon emission prediction and the response capacity to actual working condition changes are remarkably improved.

The invention discloses a regional integrated energy system optimization method and system, and the method comprises the steps: S1, constructing a multi-energy demand response model, and carrying out the linkage scheduling among multi-energy loads based on the multi-energy demand response model; s2, designing and optimizing a stepped carbon transaction mechanism; and S3, performing multi-objective optimization and cooperative operation. According to the invention, through a three-layer optimization architecture of multi-energy demand response, stepped carbon transaction and multi-target cooperation, economic, low-carbon and efficient cooperation optimization of the regional integrated energy system is realized, and the blank of the traditional technology in the aspects of multi-energy flow linkage scheduling, dynamic carbon price excitation and new energy refined consumption is filled.

The invention provides a privacy protection multi-energy load day-ahead probability prediction method based on a diffusion model, and belongs to the field of multi-energy load prediction of an integrated energy system. Comprising the following steps: S1, acquiring respective energy consumption load data by a local main body, and preprocessing and normalizing the data; s2, dividing the energy consumption data into historical loads and labels, and respectively training two groups of self-encoders-self-decoders; s3, the local main body uploads the latent variables obtained after coding to the cloud; s4, grouping and integrating latent variables by the diffusion model of the cloud, and constructing a joint probability prediction model; and S5, during model reasoning, after a latent variable generated by the cloud is downloaded locally, a final predicted value is obtained through decoding by a self-decoder. According to the method, a prediction framework based on longitudinal federated learning is designed for a multi-energy load probability prediction scene related to a cross-energy form in an integrated energy system, and a diffusion model capable of predicting multi-energy load joint probability distribution is constructed by considering a coupling relationship among multi-energy loads.

The invention discloses a multi-energy load prediction method based on feature screening and multi-model fusion, and belongs to the field of electric power and comprehensive energy load prediction. The invention provides a three-stage hybrid learning prediction framework. In the first stage, dynamic feature screening is achieved through a recursive feature elimination cross validation method based on expert knowledge constraints, key meteorological elements and multi-energy load time sequence features are reserved, and redundant feature interference is reduced. In the second stage, a multi-task long-short-term memory network is constructed, and coupling relation modeling and collaborative prediction of cold, heat and electricity multi-energy loads are achieved through sharing time sequence characteristic representation and a task exclusive output structure. And in the third stage, a random forest is adopted to carry out nonlinear correction on the residual error of the sub-model, so that the precision and robustness of prediction in sudden disturbance and local non-stationary scenes are improved, the prediction error is effectively reduced, and the stability of multi-energy load prediction is improved. And reliable support is provided for optimized operation, scheduling decision and renewable energy consumption of the park integrated energy system.

The invention discloses a multi-energy system carbon emission accounting method and system based on a block chain, and the method comprises the steps: obtaining multi-energy load data, and designing a multi-energy system frame according to the multi-energy load data; based on a multi-energy system framework, designing an emission model and a physical model of carbon emission of different energy sources on a user side, and obtaining carbon emission data of the different energy sources on the user side; a block chain technology is introduced to verify, consensus and store carbon emission data. According to the invention, the accuracy, real-time performance and transparency of carbon emission accounting are improved; and the credibility of the data is enhanced through the block chain technology. And important contributions are made for promoting global carbon emission reduction and sustainable development targets.

The invention relates to a multi-energy-load scene generation method and device based on a diffusion model. Multi-energy load data and condition information are input into a time-space and cross-dimension decomposition reconstruction network of a classifier-free condition diffusion architecture; projecting the multi-energy load data into time sequence embedding and cross-dimension feature embedding; coding the condition signal and the diffusion step number together to obtain a condition embedding vector; the reconstruction network comprises a time channel and a feature channel, a global context relationship is captured by an encoder, and condition information is fused based on a condition embedding vector through a decoder; wherein an STAR module is arranged behind the decoder of the feature channel; and integrating the output of the time channel and the output of the feature channel, and then carrying out layer-by-layer denoising to generate a multi-energy-load scene. Compared with the prior art, the method has the advantages that autocorrelation, cross correlation and dislocation correlation among cold, hot and electric loads of the building group and renewable energy output are considered, and high-fidelity scene generation under condition control is realized.

The invention belongs to the technical field of energy system planning, and particularly relates to a park integrated energy system stochastic planning method and system based on multiple uncertainties, and the planning method comprises the steps: building a probability model of a multi-energy load growth rate based on park industrial planning and historical data; utilizing Monte Carlo simulation and K-means clustering to generate a representative load scene tree; establishing an upper and lower boundary prediction model of the energy price and the equipment cost by adopting a quantile regression forest method; constructing a multi-stage collaborative optimization model taking the minimum comprehensive cost expectation as a target, and considering constraint conditions such as power flow, operation, time sequence and space; and carrying out reverse recursion solution by utilizing a dynamic programming algorithm, and outputting an optimal equipment configuration and construction scheme of each stage. According to the method, the problems of load increase unpredictability and energy market price fluctuation risk in different development stages of the park energy system are solved by combining scene analysis, data-driven modeling and a dynamic optimization mechanism.

The invention provides a comprehensive energy system multi-element load prediction method based on double-layer decomposition and reconstruction and TECNFormer, and the method comprises the steps: obtaining standardized input data, meteorological factors and time characteristics, which are obtained through the preprocessing of a historical multi-energy load sequence of a comprehensive energy system, wherein the historical multi-energy load sequence comprises an electric load sequence, a cold load sequence and a heat load sequence; performing double-layer modal decomposition on the standardized input data, and classifying the standardized input data into different types of dynamic components; respectively inputting the dynamic components into a time sequence enhanced convolution module, and extracting multi-scale local features by combining multiple heterogeneous differential convolution operators with causal convolution; taking a TECNFormer composed of a time sequence enhanced convolution module and an improved long sequence prediction network as a unified shared feature extraction layer, combining the improved long sequence prediction network with a bidirectional long-short-term memory network and a sparse attention mechanism to capture long-range dependence and local details, and obtaining joint modeling features; on the basis of a hard shared network architecture, a multi-task branch is arranged at an output end, and electric, cold and heat load prediction results are synchronously output.

The invention provides an energy distribution optimization method and device, electronic equipment and a storage medium, and can be applied to the technical field of artificial intelligence. The method comprises the following steps: acquiring energy purchase cost data and energy supply attribute data of an energy supplier in a target scheduling period and energy demand attribute data and energy constraint attribute data associated with each energy load area; the energy purchase cost data, the energy supply attribute data, the energy demand attribute data and the energy constraint attribute data are processed based on an energy distribution prediction model, energy distribution information is obtained, and the energy distribution prediction model comprises a first-stage distribution sub-model constructed based on a multi-objective optimization function and a constraint condition set; and determining a target energy distribution scheme corresponding to the energy load area according to the energy distribution information. Cooperative optimization of key targets of cost, reliability and timeliness is realized in a target scheduling period, and the overall robustness and feasibility of an energy distribution scheme in a complex and uncertain environment are remarkably improved.

The present invention provides a method for optimizing energy storage configuration of an integrated energy system, comprising obtaining and preprocessing multi-energy load data, energy storage device status data, and external environment data of the integrated energy system; using an improved variational mode decomposition method to decompose the preprocessed multi-energy load data to obtain the frequency components of the multi-energy load; constructing a configuration scheme for the energy storage device of the integrated energy system based on the obtained frequency components of the multi-energy load; determining constraints, constructing an energy storage optimization objective function, and combining the preprocessed energy storage device status data and external environment data, using an improved multi-objective particle swarm optimization algorithm to find the optimal solution for the energy storage optimization objective function, and further optimizing the configuration scheme of the energy storage device of the integrated energy system based on the found optimal solution. Implementation of the present invention can effectively cope with the complex coupled fluctuations of multi-energy loads in the integrated energy system, significantly improve the energy supply reliability of the system, and optimize the energy storage configuration effect.

The invention belongs to the technical field of comprehensive energy management, and provides a comprehensive energy management and control method and system, a storage medium and electronic equipment, and the method comprises the steps: generating a power generation prediction power sequence corresponding to a preset time length based on historical power generation data through employing a first prediction algorithm; based on the historical energy consumption data of the plurality of energy consumption devices, using a second prediction algorithm to generate a device prediction load sequence corresponding to a preset duration; constructing a load scheduling algorithm of the adjustable load equipment, and generating a load scheduling model according to the power generation prediction power sequence and the equipment prediction load sequence; constructing a scheduling optimization algorithm of energy supply and energy load, and generating a scheduling optimization model based on the power generation prediction power sequence, the equipment prediction load sequence and the load scheduling model; and based on the load scheduling model and the scheduling optimization model, managing and controlling the energy consumption condition of the energy consumption equipment in a future preset duration range. The flexibility and the accuracy of load scheduling are improved; the solving efficiency is improved, and the precision of an optimization result is ensured.

The invention discloses a rural energy system multi-energy load scene construction method, and relates to the field of comprehensive energy, and the method comprises the steps: obtaining a historical multi-energy load time sequence of a rural energy system; according to the historical multi-energy load time sequence, a scene generation model is adopted to generate a multi-energy load scene set in a future time period; the scene generation model is constructed based on a Transform model and a regularization relative loss generative adversarial network, and a cosine annealing algorithm and a hot restart mechanism are adopted to dynamically adjust the learning rate when the scene generation model is trained; and reducing scenes in the multi-energy load scene set by adopting a dynamic time warping distance method and a K-Medoids clustering method to obtain a typical multi-energy load representative scene set. According to the method, on the basis that the generated scene keeps a certain diversity, the historical data evolution law and the coupling characteristic between the source and the load are restored to the maximum extent, and extraction and reduction of the representative scene are achieved.

The invention discloses a comprehensive energy load short-term prediction method, system and device and a medium, and the method comprises the steps: screening meteorological factors which affect a load through employing a Pearson correlation coefficient, and determining a meteorological sensitive factor; calculating the similarity between the historical dates and the to-be-predicted dates through a grey correlation analysis algorithm, selecting a plurality of historical dates with the highest similarity, and constructing a similar day training set; carrying out adaptive decomposition on the load sequence by adopting an improved variational mode decomposition algorithm to obtain an intrinsic mode function component set; optimizing core parameters of the support vector machine model through an improved sparrow search algorithm, and predicting each sub-component by using the optimized model to obtain a predicted value of each sub-component; and adding and merging the sub-component prediction values of the same load type according to a time sequence to obtain a comprehensive energy load short-term prediction value. The method effectively improves the precision, stability and adaptability of short-term load prediction of the integrated energy system.

The invention discloses a zero-carbon energy storage building comprehensive illumination energy load optimization scheduling method, and relates to the technical field of power grid coupling scheduling, and the method comprises the steps: recognizing a reflected light path through a light path random sampling simulation method based on a building photo-thermal environment feature matrix, and constructing a building surface light intensity distribution thermodynamic diagram; according to the building surface light intensity distribution thermodynamic diagram, the deflection angle of the building reflector array is dynamically adjusted, and a building light guide performance parameter set is generated through a dynamic light field efficiency quantification method; according to the building light guide performance parameter set, a hybrid MPPT algorithm is adopted to obtain a photovoltaic operation state data set; building energy hierarchical scheduling is carried out based on the photovoltaic operation state data set, and an energy scheduling instruction set and a cross-domain energy scheduling collaborative log are obtained; according to the method, the photovoltaic operation state data set and the power grid hierarchical framework are fused through the building energy hierarchical scheduling model, cross-space-time collaborative optimization of the illumination energy and the power grid load is realized, and the target of zero-carbon building light-storage-network full-link efficient scheduling is achieved.

Disclosed is a method for constructing an optimal scheduling model of an integrated energy system considering virtual thermal storage, which relates to the technical field of electric power systems. The method includes: establishing an operation model of an apparatus, a demand response model of an energy load, a charging model of an electric vehicle, and an energy flow model of a heat supply network of the virtual thermal storage in the integrated energy system; processing, according to a second-order cone relaxation method, a direct current power flow model of a power distribution network, and constructing the optimal scheduling model of the integrated energy system by taking minimization of total cost of an energy operator as an objective, thereby coordinately and optimally scheduling a plurality of energy sources.

The invention discloses a multi-park integrated energy system collaborative optimization method based on an equivalent projection theory. The method comprises the following steps: establishing a park integrated energy system scheduling model considering fuel gas hydrogen doping; establishing a park integrated energy system robust collaborative scheduling model participating in the multi-park integrated energy system alliance by considering the uncertainty of hydrogen energy interaction and energy load; the established double-layer region integrated energy system robust collaborative scheduling model is equivalent to a single-layer mixed integer programming collaborative scheduling model based on an equivalent projection theory; deriving a decentralized solution of the established mixed integer programming collaborative scheduling model by using a dual decomposition algorithm; and establishing a multi-park integrated energy system collaborative optimization model based on the feasible region equivalent projection and solving the multi-park integrated energy system collaborative optimization model. According to the method, a two-stage robust problem is converted into an MILP type robust optimization problem through feasible region projection, the converted model does not need to be iteratively solved, and the solving time is greatly shortened compared with that of a traditional iterative method; and a decentralized solution is derived by using a dual decomposition algorithm, so that the convergence and optimality of the result are ensured.

The invention provides an integrated energy system resource robust planning method considering the low-carbon benefit of a new energy automobile, and the method comprises the steps: constructing an integrated energy system frame on the basis of analyzing the electricity-heat-cold-hydrogen energy conversion, flow and equipment operation characteristics; the new energy automobile is researched to replace a fuel automobile to run, the energy charging requirement of the new energy automobile is simulated, and a new energy automobile carbon-green certificate quota model is constructed; the uncertainty of new energy output and multi-energy load requirements is considered, and a min-max-min two-stage robust planning model is established by taking system planning economy optimization as a target. According to the comprehensive energy system resource robust planning method considering the low-carbon benefit of the new energy automobile provided by the invention, the IES resource robust planning is realized while the system planning-operation economy is optimized.

The invention relates to the technical field of energy management, in particular to a multi-source heterogeneous energy load prediction method and system for a manufacturing enterprise cluster, and the method comprises the steps: collecting original energy data from a multi-source heterogeneous system in the manufacturing enterprise cluster and external environment information related to the energy load change, preprocessing the original energy data and the external environment information to obtain standardized energy time sequence data and final external environment information; based on the standardized energy time sequence data, the final external environment information and a pre-established equipment information model, constructing a triple heterogeneous atlas, and extracting structural semantic features of each node in the triple heterogeneous atlas; based on the structure semantic features, constructing a coupling sequence diagram, modeling the coupling sequence diagram, and identifying key factors; and inputting the structure semantic features and the key factors into a multi-scale prediction model, and outputting hourly load prediction curves of various energy media in a future prediction period.

The invention belongs to the related technical field of virtual power plant resource scheduling optimization, and particularly relates to an energy load collaborative optimization method for a data center to participate in demand response, and the method comprises the steps: constructing a system model comprising N geographically distributed virtual power plants, and discretizing the operation of each virtual power plant into T scheduling periods, each virtual power plant independently manages a local resource combination thereof, and comprises a data center with a power demand, a battery energy storage system for providing a time scale energy regulation capability and a photovoltaic system for supplying renewable energy sources; and solving a second-order cone programming problem taking the minimum total net cost as a target, including a second-order cone programming problem taking the minimum total net cost when each virtual power plant operates independently and a second-order cone programming problem taking the minimum total net cost when the virtual power plants cooperate with the alliance as a target, and realizing energy-load collaborative optimization. According to the method, the operation cost is reduced while the tracking effect based on the CDL type demand response target trajectory is improved.

The invention relates to the technical field of power system dispatching, in particular to a virtual power plant optimal dispatching system and method based on a block chain, and aims to solve the problem that in the prior art, high-precision prediction of the generating capacity and load of a virtual power plant cannot be realized by using an LSTM (Long Short Term Memory) to capture a time sequence long-term dependency relationship. Input features and the number of LSTM units cannot be adjusted to adapt to different scenes, and virtual power plant operation and resource configuration are not facilitated; according to the invention, high-precision prediction of the generating capacity and the load of the virtual power plant is realized by using an LSTM algorithm through an energy load joint prediction module, the long-term dependency relationship of a time sequence is captured by using an LSTM to improve the prediction accuracy, input features and the number of LSTM units can be adjusted to adapt to different scenes, multi-strategy guarantee convergence is adopted for training to prevent overfitting, and the prediction accuracy is improved. And finally outputting a key prediction value, thereby facilitating virtual power plant operation and resource configuration.

A carbon emission dynamic prediction method and system based on multi-energy load data, the method comprising: collecting and preprocessing the historical data of carbon emission, energy consumption and multi-energy load consumption of the park, calculating the joint correlation coefficient and screening the energy type; calculating the initial carbon emission-energy conversion matrix and energy-multi-energy load conversion matrix; constructing a carbon emission coupling objective function, solving and updating the corresponding conversion matrix by alternately fixing the carbon emission-energy and energy-multi-energy load conversion matrix; establishing a carbon emission coupling model, calculating the prediction carbon emission error, constructing a prediction error correction objective function, and correcting the conversion matrix; predicting the multi-energy load consumption in the M+1 month and correcting the error, combining the corrected conversion matrix in S4, and predicting the carbon emission in the M+1 month. The present application can dynamically adjust the conversion relationship between carbon emission and multi-energy load, significantly improve the accuracy of carbon emission prediction and the response ability to actual working condition changes.

The invention discloses a multi-energy collaborative predictive control method and system based on digital twinning, and relates to the technical field of industrial control. The method comprises the following steps: inputting each energy historical load of a plurality of historical periods and meteorological prediction data of a next period into a trained LSTM prediction model to carry out multi-energy load prediction, and outputting to obtain each energy load prediction value of the next period; constructing an optimization model, and solving the optimization model to obtain a preliminary scheduling plan; performing simulation deduction on the control instruction in the preliminary scheduling plan to obtain a pre-estimated system state in the next period; if the simulation deduction result is deviated from expectation, correcting the preliminary scheduling plan based on the state of a next-period pre-estimated system to obtain a final control instruction set; the problem of endogenous feedback circulation caused by the fact that prediction depends on a control execution result in existing multi-energy control is solved, sawtooth-shaped deviation between a predicted value and an actual value is avoided, and safe and efficient operation of a multi-energy system is achieved.

The invention discloses a wind power-containing electric power system source load cooperative scheduling method considering a high energy load, and the method comprises the steps: analyzing the scheduling demands of a wind power-containing electric power system, building an electric arc furnace load and wind power output uncertainty model based on the schedulable characteristics of the electric arc furnace load, generating a scene through employing a data driving method, and carrying out the calculation of the scene. The method comprises the following steps: analyzing uncertainty superposition influence of electric arc furnace load and wind power output, determining reserve capacity required by an electric power system to cope with uncertainty fluctuation, proposing a smelting type high-energy load adaptive electricity price mechanism considering wind power output change and source load fluctuation, establishing an optimal scheduling model, and solving the optimal scheduling model by adopting an artificial bee colony algorithm; according to the method, powerful theoretical support is provided for the smelting type high-energy load to participate in power grid dispatching and wind power absorption, the accuracy of power grid dispatching is guaranteed, and absorption of new energy and safe operation of a power system are promoted.

A system and method for seasonal energy consumption determination using verified energy loads with the aid of a digital computer are provided. A digital computer is operated, including: obtaining energy loads for a building measured over a seasonal time period; obtaining outdoor temperatures for the building as measured over the seasonal time period; verifying stability of the energy loads, including: evaluating the energy loads over time; and identifying at least one of one or more discontinuities and one or more irregularities in the energy loads based on the evaluation. Operating the computer further includes: determining a baseload energy consumption using at least some of those of the energy loads; calculating seasonal fuel consumption rates and balance point temperatures; and disaggregating seasonal fuel consumption based on the baseload energy consumption, seasonal fuel consumption rates, balance point temperatures, and at least some of the outdoor temperatures into component loads of consumption.

The invention discloses a load translation control method for a hybrid diesel locomotive considering a diesel engine and a gas engine. The method comprises the following steps: in a ground bench test, respectively simulating typical operation conditions of the diesel / gas engine, and determining respective safe power fluctuation intervals; in the running process of the hybrid diesel locomotive, the output power of the diesel / gas engine is dynamically corrected based on the power fluctuation threshold value of the running data; and scene-based multi-energy load translation control is carried out, the scene comprises the dynamic operation characteristics of acceleration and deceleration of the train, and the engine power of the hybrid diesel locomotive is adjusted according to the working mode of the power battery and the power fluctuation difference value, so that the load translation of the hybrid diesel locomotive is met based on the engine power. According to the method, multi-energy cooperative control considering load translation, scientific and controllable threshold value and working condition adaptation is achieved, the comprehensive traction force of the hybrid diesel locomotive is improved, and energy conservation and emission reduction are achieved.