2,103 research outputs found
Multi-objective particle swarm optimization algorithm for multi-step electric load forecasting
As energy saving becomes more and more popular, electric load forecasting has played a more and more crucial role in power management systems in the last few years. Because of the real-time characteristic of electricity and the uncertainty change of an electric load, realizing the accuracy and stability of electric load forecasting is a challenging task. Many predecessors have obtained the expected forecasting results by various methods. Considering the stability of time series prediction, a novel combined electric load forecasting, which based on extreme learning machine (ELM), recurrent neural network (RNN), and support vector machines (SVMs), was proposed. The combined model first uses three neural networks to forecast the electric load data separately considering that the single model has inevitable disadvantages, the combined model applies the multi-objective particle swarm optimization algorithm (MOPSO) to optimize the parameters. In order to verify the capacity of the proposed combined model, 1-step, 2-step, and 3-step are used to forecast the electric load data of three Australian states, including New South Wales, Queensland, and Victoria. The experimental results intuitively indicate that for these three datasets, the combined model outperforms all three individual models used for comparison, which demonstrates its superior capability in terms of accuracy and stability
Applicability of End Use Method for Long Term Load Forecasting of Islands
Electric load forecasting of a nation, state or a city is very important for their growth. The consumption of electricity is directly proportional to the economic development of a region. Long term electric load forecasting gives you the idea of load requirement after 5 years or 10 years or 15 years. This forecast helps the administrators for taking action to meet the electric requirement. Accuracy of this assessment will fulfill the main objective of maintaining the supply and demand of electricity. Among the various methods of long term electric load forecasting End Use method is one of the mostly used methods. It gives good result for big states and countries which are economically sound, with rich natural recourses and well connected with other states. This work finds out the applicability of End Use method for long term load forecasting of remote islands. Anadaman Nicober Islands is taken as a case study. The result shows that when forecasting goes beyond five, six years the errors are not acceptable. This work gives the direction to the administrators and researchers to explore other methods, may be some hybrid methods for long term forecasting of Islands
Short-term electric load forecasting using computational intelligence methods
Accurate time series forecasting is a key issue to support individual and organizational decision making. In this paper, we introduce several methods for short-term electric load forecasting. All the presented methods stem from computational intelligence techniques: Random Forest, Nonlinear Autoregressive Neural Networks, Evolutionary Support Vector Machines and Fuzzy Inductive Reasoning. The performance of the suggested methods is experimentally justified with several experiments carried out, using a set of three time series from electricity consumption in the real-world domain, on different forecasting horizons
Probabilistic electric load forecasting through Bayesian Mixture Density Networks
Probabilistic load forecasting (PLF) is a key component in the extended
tool-chain required for efficient management of smart energy grids. Neural
networks are widely considered to achieve improved prediction performances,
supporting highly flexible mappings of complex relationships between the target
and the conditioning variables set. However, obtaining comprehensive predictive
uncertainties from such black-box models is still a challenging and unsolved
problem. In this work, we propose a novel PLF approach, framed on Bayesian
Mixture Density Networks. Both aleatoric and epistemic uncertainty sources are
encompassed within the model predictions, inferring general conditional
densities, depending on the input features, within an end-to-end training
framework. To achieve reliable and computationally scalable estimators of the
posterior distributions, both Mean Field variational inference and deep
ensembles are integrated. Experiments have been performed on household
short-term load forecasting tasks, showing the capability of the proposed
method to achieve robust performances in different operating conditions.Comment: 56 page
Electric Load Forecasting Using Long Short-term Memory Algorithm
Abstract
Power system load forecasting refers to the study or uses a mathematical method to process
past and future loads systematically, taking into account important system operating
characteristics, capacity expansion decisions, natural conditions, and social impacts, to
meet specific accuracy requirements. Dependence of this, determine the load value at a
specific moment in the future. Improving the level of load forecasting technology is
conducive to the planned power management, which is conducive to rationally arranging
the grid operation mode and unit maintenance plan, and is conducive to formulating
reasonable power supply construction plans and facilitating power improvement, and
improve the economic and social benefits of the system.
At present, there are many methods for load forecasting. The newer algorithms mainly
include the neural network method, time series method, regression analysis method,
support vector machine method, and fuzzy prediction method. However, most of them do
not apply to long-term time-series predictions, and as a result, the prediction accuracy for
long-term power grids does not perform well.
This thesis describes the design of an algorithm that is used to predict the load in a long
time-series. Predict the load is significant and necessary for a dynamic electrical network.
Improved the forecasting algorithm can save a ton of the cost of the load. In this paper, we
propose a load forecasting model using long short-term memory(LSTM). The proposed
implementation of LSTM match with the time-series dataset very well, which can improve
the accuracy of convergence of the training process. We experiment with the difference
time-step to expedites the convergence of the training process. It is found that all cases
achieve significant different forecasting accuracy while forecasting the difference timesteps.
Keywords—Load forecasting, long short-term memory, micro-gri
Evaluating of Short-Term Electrical Load Forecasting System Using Fuzzy Logic Control: A Study Case in Sudan
Generation, Transmission and Distribution sections of the electric power grid system are a function of electric load forecasting. This is because, many benefits can be obtained by using load forecasting, such as reduction in the generating cost and increasing the reliability of the power system due to improving energy management. The objective of this study is therefore to design a fuzzy logic system for short-term electric load forecasting to reduce power losses particularly in times where the electric power generation is higher than the electric load demand. In this study, the independent variables that were applied to the developed short-term load forecasting Simulink model were time, temperature, and similar previous electric day load demand, and they were collected from the specific area load control center in Sudan. Fuzzy rules were prepared using Mamdani implication. The obtained fuzzy logic results were compared with the actual load demand, and it was found that there was an error that ranged between 12% and 0.09%
Supervised Machine Learning Techniques for Short-Term Load Forecasting
Electric Load Forecasting is essential for the utility companies for energy management based on the demand. Machine Learning Algorithms has been in the forefront for prediction algorithms. This Thesis is mainly aimed to provide utility companies with a better insight about the wide range of Techniques available to forecast the load demands based on different scenarios. Supervised Machine Learning Algorithms were used to come up with the best possible solution for Short-Term Electric Load forecasting. The input Data set has the hourly load values, Weather data set and other details of a Day. The models were evaluated using MAPE and R2 as the scoring criterion. Support Vector Machines yield the best possible results with the lowest MAPE of 1.46 %, a R2 score of 92 %. Recurrent Neural Networks univariate model serves its purpose as the go to model when it comes to Time-Series Predictions with a MAPE of 2.44 %. The observations from these Machine learning models gives the conclusion that the models depend on the actual Data set availability and the application and scenario in pla
Interactive Generalized Additive Model and Its Applications in Electric Load Forecasting
Electric load forecasting is an indispensable component of electric power
system planning and management. Inaccurate load forecasting may lead to the
threat of outages or a waste of energy. Accurate electric load forecasting is
challenging when there is limited data or even no data, such as load
forecasting in holiday, or under extreme weather conditions. As high-stakes
decision-making usually follows after load forecasting, model interpretability
is crucial for the adoption of forecasting models. In this paper, we propose an
interactive GAM which is not only interpretable but also can incorporate
specific domain knowledge in electric power industry for improved performance.
This boosting-based GAM leverages piecewise linear functions and can be learned
through our efficient algorithm. In both public benchmark and electricity
datasets, our interactive GAM outperforms current state-of-the-art methods and
demonstrates good generalization ability in the cases of extreme weather
events. We launched a user-friendly web-based tool based on interactive GAM and
already incorporated it into our eForecaster product, a unified AI platform for
electricity forecasting
Deep learning for time series forecasting: The electric load case
Management and efficient operations in critical infrastructures such as smart grids take huge advantage of accurate power load forecasting, which, due to its non-linear nature, remains a challenging task. Recently, deep learning has emerged in the machine learning field achieving impressive performance in a vast range of tasks, from image classification to machine translation. Applications of deep learning models to the electric load forecasting problem are gaining interest among researchers as well as the industry, but a comprehensive and sound comparison among different-also traditional-architectures is not yet available in the literature. This work aims at filling the gap by reviewing and experimentally evaluating four real world datasets on the most recent trends in electric load forecasting, by contrasting deep learning architectures on short-term forecast (one-day-ahead prediction). Specifically, the focus is on feedforward and recurrent neural networks, sequence-to-sequence models and temporal convolutional neural networks along with architectural variants, which are known in the signal processing community but are novel to the load forecasting one
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