Short-term load forecast using ensemble neuro-fuzzy model

Malekizadeh, M.; Karami, Hossein; Karimi, M.; Moshari, Amir; Sanjari, M. J.

doi:10.1016/j.energy.2020.117127

Cited by 52 publications

(17 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…eir article compared the international tourism market prediction models by predicting the quarterly flow of tourists from the four major tourism markets at that time, namely, the United States, Japan, the United Kingdom, and New Zealand to Australia. e international tourism market prediction models they used include error correction model, autoregressive model, autoregressive moving average combination model, basic structure model, and time series regression model [8]. e relevant information of tourism demand level is very important for commercial institutions and government decision-making departments.…”

Section: Introductionmentioning

confidence: 99%

Tourist Demand Prediction Model Based on Improved Fruit Fly Algorithm

Guo

Shang

2021

Security and Communication Networks

View full text Add to dashboard Cite

To accurately predict the development and change trend of the future, tourism market can effectively improve the planning and purpose of tourism development. In order to improve the accuracy of tourist demand prediction, this paper studies the tourist demand prediction model based on improved fruit fly algorithm. Aiming at the optimization defects of the traditional fruit fly optimization algorithm (FOA), the model introduces two concepts of sensitivity and pheromone, improves the optimization strategy and position replacement of fruit fly, improves the diversity of fruit fly population, modifies the global optimization characteristics of the algorithm, and improves the local search ability and search efficiency of the algorithm. By combining the improved AFOA with echo state network (ESN), a two-stage combined prediction model (AAFOA-ESN) is constructed. The experimental results show that the minimum prediction error accuracy of the model is only 0.55%, which has more robust prediction effect, faster convergence speed, and higher prediction accuracy.

show abstract

Section: Introductionmentioning

confidence: 99%

Tourist Demand Prediction Model Based on Improved Fruit Fly Algorithm

Guo

Shang

2021

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…The input sequence x[1], ..., x[T]is divided into smaller segments, which enable GRU to discover the internal periodicity in the consumption data. The segments pass through the GRU [33] to extract timing characteristics of the input vector obtain the prediction sequence y[1], ... , y[N]. The schematic diagram of a sliding-window process is shown in Figure 6.…”

Section: Input X[t-n+1] X[t-n+2] X[t-2] X[t-n+1] Y[t]'mentioning

confidence: 99%

Evaluation Model of Operation State Based on Deep Learning for Smart Meter

Zhao

Han

et al. 2021

Energies

View full text Add to dashboard Cite

The operation state detection of numerous smart meters is a significant problem caused by manual on-site testing. This paper addresses the problem of improving the malfunction detection efficiency of smart meters using deep learning and proposes a novel evaluation model of operation state for smart meter. This evaluation model adopts recurrent neural networks (RNN) to predict power consumption. According to the prediction residual between predicted power consumption and the observed power consumption, the malfunctioning smart meter is detected. The training efficiency for the prediction model is improved by using transfer learning (TL). This evaluation uses an accumulator algorithm and threshold setting with flexibility for abnormal detection. In the simulation experiment, the detection principle is demonstrated to improve efficient replacement and extend the average using time of smart meters. The effectiveness of the evaluation model was verified on the actual station dataset. It has accurately detected the operation state of smart meters.

show abstract

“…Forecasting techniques employed to load prediction can be classified into two primary categories, including traditional statistical methods and artificial intelligent (AI) based methods. Traditional statistical methods, widely used in load prediction, contain the Auto-Regressive-Integrated-Moving-Average (ARIMA) models [20][21][22][23][24], time series regression models [25][26][27][28], fuzzy models [29][30][31][32], and grey prediction models [33][34][35][36]. AIbased methods are generally adaptive and robust to non-stationary data and make the prediction results with high accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Uncertain Interval Forecasting for Combined Electricity-Heat-Cooling-Gas Loads in the Integrated Energy System Based on Multi-Task Learning and Multi-Kernel Extreme Learning Machine

Zhao

Guo

2021

Mathematics

View full text Add to dashboard Cite

The accurate prediction of electricity-heat-cooling-gas loads on the demand side in the integrated energy system (IES) can provide significant reference for multiple energy planning and stable operation of the IES. This paper combines the multi-task learning (MTL) method, the Bootstrap method, the improved Salp Swarm Algorithm (ISSA) and the multi-kernel extreme learning machine (MKELM) method to establish the uncertain interval prediction model of electricity-heat-cooling-gas loads. The ISSA introduces the dynamic inertia weight and chaotic local searching mechanism into the basic SSA to improve the searching speed and avoid falling into local optimum. The MKELM model is established by combining the RBF kernel function and the Poly kernel function to integrate the superior learning ability and generalization ability of the two functions. Based on the established model, weather, calendar information, social–economic factors, and historical load are selected as the input variables. Through empirical analysis and comparison discussion, we can obtain: (1) the prediction results of workday are better than those on holiday. (2) The Bootstrap-ISSA-MKELM based on the MTL method has superior performance than that based on the STL method. (3) Through comparing discussion, we discover the established uncertain interval prediction model has the superior performance in combined electricity-heat-cooling-gas loads prediction.

show abstract

Short-term load forecast using ensemble neuro-fuzzy model

Cited by 52 publications

References 21 publications

Tourist Demand Prediction Model Based on Improved Fruit Fly Algorithm

Tourist Demand Prediction Model Based on Improved Fruit Fly Algorithm

Evaluation Model of Operation State Based on Deep Learning for Smart Meter

Uncertain Interval Forecasting for Combined Electricity-Heat-Cooling-Gas Loads in the Integrated Energy System Based on Multi-Task Learning and Multi-Kernel Extreme Learning Machine

Contact Info

Product

Resources

About