Short-Term Industrial Load Forecasting Based on Ensemble Hidden Markov Model

Wang, Yuan‐Yuan; Kong, Yang; Tang, Xiafei; Chen, Xiaoqiao; Yao, Xu; Chen, Jun; Sun, Shanfeng; Guo, Yongsheng; Chen, Yuhao

doi:10.1109/access.2020.3020799

Cited by 25 publications

(8 citation statements)

References 48 publications

(78 reference statements)

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“…Hourly cons(j) (10) where Hourly cons(i) is the i-th value of energy consumption given by the reference dataset and Nday is the number of days per month; 3) these monthly percentage values were multiplied by the corresponding monthly consumption data of the Boarding School, already shown in Tab. 5.…”

Section: ''Collegio Enaudi'' Hourly Energy Consumption Evaluationmentioning

confidence: 99%

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An Efficient Artificial Intelligence Energy Management System for Urban Building Integrating Photovoltaic and Storage

et al. 2023

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The emerging leading role of green energy in our society pushes the investigation of new economic and technological solutions. Green energies and smart communities increase efficiency with the use of digital solutions for the benefits of inhabitants and companies. The paper focuses on the development of a methodology for the energy management, combining photovoltaics and storage systems, considering as the main case study a multi-story building characterized by a high density of households, used to generate data which allow feasibility foresights. The physical model of the algorithm is composed by two main elements: the photovoltaics modules and the battery energy storage system. In addition, to gain information about the real-time consumption a machine learning module is included in our approach to generate predictions about the near future demand. The benefits provided by the method are evaluated with an economic analysis, which computes the return of the investment using the real consumptions of a Boarding School, located in Turin (Italy). The case study analyzed in this article showed an increase in purchased energy at the minimum price from 25% to 91% and a 55% reduction in the electricity bill compared to most solutions on the market, with no additional costs and a stabilizing effect on the grid. Finally, the economic analysis shows that the proposed method is a profitable investment, with a breakeven point of thirteen years, due to the very simple implementation and the zero additional cost requested.INDEX TERMS Deep learning, energy management systems, energy storage, environmental economics, renewable energy sources. FRANCESCO GRIMACCIA (Senior Member, IEEE) received the M.S. and Ph.D. degrees (cum laude) in electrical engineering from the Politecnico di Milano, Milan, Italy, in 2003 and 2007, respectively. He is currently an Associate Professor with the Energy Department, Politecnico di Milano. His main research interest includes softcomputing technique development and applications in different electric engineering fields, such as unmanned systems, photovoltaics, and other energy-harvesting devices. He is a member of the IEEE Computational Intelligence Society and the Vice-President of the Associazione Italiana di Elettrotecnica, Elettronica and the Automazione, Informatica e Telecomunicazioni, Milan Section.

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Section: ''Collegio Enaudi'' Hourly Energy Consumption Evaluationmentioning

confidence: 99%

“…Thus, a key element for obtaining a feasible energy transition is the capability to achieve a good forecasting of RES production and electric load [8]. Many works in the literature address the problem of either short-term [10] or long-term forecasting [9].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Artificial Intelligence Energy Management System for Urban Building Integrating Photovoltaic and Storage

et al. 2023

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“…The early short-term load methods mainly use are the exponential smoothing method [5] and hidden Markov model [6], but the ability of these methods to extract nonlinear characteristics of load is weak [7]. With the rapid increase in the installation of smart meters [8] and the development of artificial intelligence technology [9], short-term load forecasting based on big data analysis has become a current research hotspot, such as the BP neural network [10], extreme learning machine [11], support vector machine [12], etc.…”

Section: Introductionmentioning

confidence: 99%

A Short-Term Load Forecasting Model Based on Crisscross Grey Wolf Optimizer and Dual-Stage Attention Mechanism

Gong

2023

Energies

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Accurate short-term load forecasting is of great significance to the safe and stable operation of power systems and the development of the power market. Most existing studies apply deep learning models to make predictions considering only one feature or temporal relationship in load time series. Therefore, to obtain an accurate and reliable prediction result, a hybrid prediction model combining a dual-stage attention mechanism (DA), crisscross grey wolf optimizer (CS-GWO) and bidirectional gated recurrent unit (BiGRU) is proposed in this paper. DA is introduced on the input side of the model to improve the sensitivity of the model to key features and information at key time points simultaneously. CS-GWO is formed by combining the horizontal and vertical crossover operators, to enhance the global search ability and the diversity of the population of GWO. Meanwhile, BiGRU is optimized by CS-GWO to accelerate the convergence of the model. Finally, a collected load dataset, four evaluation metrics and parametric and non-parametric testing manners are used to evaluate the proposed CS-GWO-DA-BiGRU short-term load prediction model. The experimental results show that the RMSE, MAE and SMAPE are reduced respectively by 3.86%, 1.37% and 0.30% of those of the second-best performing CSO-DA-BiGRU model, which demonstrates that the proposed model can better fit the load data and achieve better prediction results.

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“…The examination of the supply and demand status in the power system begins with power load forecasting as its foundation. Efficient and accurate power load forecasting helps to maintain the secure and consistent supply of electricity by keeping a close eye on the balance between supply and demand in real time [5]. Forecasting power consumption in the near future is an integral part of power load prediction.…”

Section: Introductionmentioning

confidence: 99%

A Short-term Electricity Load Forecasting Method Based on WOA-BiLSTM-Attention

Song

Lan²,

Luo

et al. 2023

J. Phys.: Conf. Ser.

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Under the “double carbon” policy, it is time to build a new green and safe power system. Special attention should be paid to the low accuracy in short-term power load prediction. Therefore, this paper presents a technique for anticipating short-term power demand based on a bidirectional long short-term memory network with a whale-optimized attention mechanism (WOA-BiLSTM-Attention), which is used to forecast and analyze the measured power values in a certain area. The experimental findings reveal that the suggested technique has much greater prediction accuracy and convergence speed, as well as superior stability, when compared to LSTM, BiLSTM, and BiLSTM based on attention mechanism, making it a good reference for power system planning and stability.

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Short-Term Industrial Load Forecasting Based on Ensemble Hidden Markov Model

Cited by 25 publications

References 48 publications

An Efficient Artificial Intelligence Energy Management System for Urban Building Integrating Photovoltaic and Storage

An Efficient Artificial Intelligence Energy Management System for Urban Building Integrating Photovoltaic and Storage

A Short-Term Load Forecasting Model Based on Crisscross Grey Wolf Optimizer and Dual-Stage Attention Mechanism

A Short-term Electricity Load Forecasting Method Based on WOA-BiLSTM-Attention

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