Designing a short-term load forecasting model in the urban smart grid system

Li, Chen

doi:10.1016/j.apenergy.2020.114850

Cited by 56 publications

(25 citation statements)

References 51 publications

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“…The short-term electricity load forecasting is implemented to solve a wide range of needs, providing a wide range of applications. The most evident difference between research is the load scale, from a single transformer [9] to buildings [10], to cities [11], regions [12], and even countries [13]. The second most crucial distinction among the research field is the forecasting horizon.…”

Section: Literature Reviewmentioning

confidence: 99%

Short-Term Electricity Load Forecasting with Machine Learning

Madrid

António

2021

Information

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An accurate short-term load forecasting (STLF) is one of the most critical inputs for power plant units’ planning commitment. STLF reduces the overall planning uncertainty added by the intermittent production of renewable sources; thus, it helps to minimize the hydrothermal electricity production costs in a power grid. Although there is some research in the field and even several research applications, there is a continual need to improve forecasts. This research proposes a set of machine learning (ML) models to improve the accuracy of 168 h forecasts. The developed models employ features from multiple sources, such as historical load, weather, and holidays. Of the five ML models developed and tested in various load profile contexts, the Extreme Gradient Boosting Regressor (XGBoost) algorithm showed the best results, surpassing previous historical weekly predictions based on neural networks. Additionally, because XGBoost models are based on an ensemble of decision trees, it facilitated the model’s interpretation, which provided a relevant additional result, the features’ importance in the forecasting.

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Section: Literature Reviewmentioning

confidence: 99%

Short-Term Electricity Load Forecasting with Machine Learning

Madrid

António

2021

Information

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“…In order to improve the accuracy of short-term power load forecasting. Chen Li [12] used a denoising method based on decomposition and reconstruction, and used the multi-objective optimization algorithm (MOGOA) to optimize the parameters of the artificial neural network. Sybil Sharvelle et al [13] developed and demonstrated the Integrated Urban Water Model (IUWM) for forecasting urban water demand with options to assess effects of water conservation and reuse, and the capacity of IUWM for the assessment of the spatiotemporal variability of water consumption.…”

Section: Literature Reviewmentioning

confidence: 99%

Bi-level Planning Model for Urban Energy Steady State Optimal Configuration

Wang

Zhou

Dong

et al. 2020

Preprint

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BackgroundWith the rapid development of social economy, energy consumption has continued to grow, and the contradiction between energy supply and demand has become increasingly tense. Cities account for two-thirds of global primary energy demand that makes urban energy systems become a center of sustainable transitions.MethodsThis paper builds a bi-level planning model for steady state optimal configuration to realize the reasonable planning of the urban energy structure. The first level mainly analyzes the relationship between coal, petroleum, natural gas, and renewable energy when the energy system is in a stable state. Based on the steady-state relationship of multiple energy sources, the second level is mainly aiming at energy allocation of the city, to achieve the minimization of construction and operating costs of the urban energy system, as well as the pollutant emissions. And nonlinear system dynamics and the non-dominated sorting genetic algorithm II (NSGA-II) algorithm are implemented to solve the model.ResultsThe model studies the interaction between four energy sources to find out the demand of each energy source when the urban energy system is in a stable state. To illustrate the advantage of the method, the application of the planning model proposed in this paper is discussed in the energy system of city J of China. Under the premise of ensuring the stability of the urban energy system, the city J has two energy planning schemes: mainly coal or mainly high-quality energy.ConclusionThe coal-based energy structure can save economic costs. However, from the perspective of actual demand or ecological environment, the urbanization level of city J is relatively high, and the coal-based energy structure is unreasonable and may cause greater environmental pollution. Under the type of high-quality energy development model, city J should continue to strengthen the adjustment of the industrial structure and optimize and upgrade the urban development path. The focus is on strengthening the use of clean energy and improving energy efficiency, thereby further reducing the demand for traditional fossil energy in the process of economic development.

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“…e neural networks are then trained using these features and the outputs of these trained neural networks are known as the forecasted load. In [25], a data mining and artificial neural network optimized by multiobjective grasshopper and phase space reconstruction method is presented. In [26], a short-term load forecasting approach that can capture variations in building operation regardless of building type and location is proposed.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting Using Neural Network and Particle Swarm Optimization (PSO) Algorithm

Chafi

Afrakhte

2021

Mathematical Problems in Engineering

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Electrical load forecasting plays a key role in power system planning and operation procedures. So far, a variety of techniques have been employed for electrical load forecasting. Meanwhile, neural-network-based methods led to fewer prediction errors due to their ability to adapt properly to the consuming load's hidden characteristic. Therefore, these methods were widely accepted by the researchers. As the parameters of the neural network have a significant impact on its performance, in this paper, a short-term electrical load forecasting method using neural network and particle swarm optimization (PSO) algorithm is proposed, in which some neural network parameters including learning rate and number of hidden layers are determined in order to forecast electrical load using the PSO algorithm precisely. Then, the neural network with these optimized parameters is used to predict the short-term electrical load. In this method, a three-layer feedforward neural network trained by backpropagation algorithm is used beside an improved gbest PSO algorithm. Also, the neural network prediction error is defined as the PSO algorithm cost function. The proposed approach has been tested on the Iranian power grid using MATLAB software. The average of three indices beside graphical results has been considered to evaluate the performance of the proposed method. The simulation results reflect the capabilities of the proposed method in accurately predicting the electrical load.

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Designing a short-term load forecasting model in the urban smart grid system

Cited by 56 publications

References 51 publications

Short-Term Electricity Load Forecasting with Machine Learning

Short-Term Electricity Load Forecasting with Machine Learning

Bi-level Planning Model for Urban Energy Steady State Optimal Configuration

Short-Term Load Forecasting Using Neural Network and Particle Swarm Optimization (PSO) Algorithm

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