Short-Term Demand Forecasting Methods for Public Bicycles Under Big Data Environment

In the smart grid (SG) environment, consumers are enabled to alter electricity consumption patterns in response to electricity prices and incentives. This results in prices that may differ from the initial price pattern. Electricity price and demand forecasting play a vital role in the reliability and sustainability of SG. Forecasting using big data has become a new hot research topic as a massive amount of data is being generated and stored in the SG environment. Electricity users, having advanced knowledge of prices and demand of electricity, can manage their load efficiently. In this paper, a recurrent neural network (RNN), long short term memory (LSTM), is used for electricity price and demand forecasting using big data. Researchers are working actively to propose new models of forecasting. These models contain a single input variable as well as multiple variables. From the literature, we observed that the use of multiple variables enhances the forecasting accuracy. Hence, our proposed model uses multiple variables as input and forecasts the future values of electricity demand and price. The hyperparameters of this algorithm are tuned using the Jaya optimization algorithm to improve the forecasting ability and increase the training mechanism of the model. Parameter tuning is necessary because the performance of a forecasting model depends on the values of these parameters. Selection of inappropriate values can result in inaccurate forecasting. So, integration of an optimization method improves the forecasting accuracy with minimum user efforts. For efficient forecasting, data is preprocessed and cleaned from missing values and outliers, using the z-score method. Furthermore, data is normalized before forecasting. The forecasting accuracy of the proposed model is evaluated using the root mean square error (RMSE) and mean absolute error (MAE). For a fair comparison, the proposed forecasting model is compared with univariate LSTM and support vector machine (SVM). The values of the performance metrics depict that the proposed model has higher accuracy than SVM and univariate LSTM.

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Study of short-term load forecasting in big data environment

2017 29th Chinese Control And Decision Conference (CCDC) ◽

10.1109/ccdc.2017.7978378 ◽

2017 ◽

Cited By ~ 2

Author(s):

Haifan Zhao ◽

Zhaohui Tang ◽

Weidong Shi ◽

Zixun Wang

Keyword(s):

Big Data ◽

Load Forecasting ◽

Short Term ◽

Short Term Load Forecasting ◽

Data Environment

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Big data analytics and demand forecasting in supply chains: a conceptual analysis

The International Journal of Logistics Management ◽

10.1108/ijlm-04-2017-0088 ◽

2018 ◽

Vol 29 (2) ◽

pp. 739-766 ◽

Cited By ~ 23

Author(s):

Erik Hofmann ◽

Emanuel Rutschmann

Keyword(s):

Big Data ◽

Data Analytics ◽

Conceptual Analysis ◽

Scientific Literature ◽

Design Science ◽

Demand Forecasting ◽

Big Data Analytics ◽

Scientific Work ◽

Content Type ◽

Forecasting Methods

Purpose Demand forecasting is a challenging task that could benefit from additional relevant data and processes. The purpose of this paper is to examine how big data analytics (BDA) enhances forecasts’ accuracy. Design/methodology/approach A conceptual structure based on the design-science paradigm is applied to create categories for BDA. Existing approaches from the scientific literature are synthesized with industry knowledge through experience and intuition. Accordingly, a reference frame is developed using three steps: description of conceptual elements utilizing justificatory knowledge, specification of principles to explain the interplay between elements, and creation of a matching by conducting investigations within the retail industry. Findings The developed framework could serve as a guide for meaningful BDA initiatives in the supply chain. The paper illustrates that integration of different data sources in demand forecasting is feasible but requires data scientists to perform the job, an appropriate technological foundation, and technology investments. Originality/value So far, no scientific work has analyzed the relation of forecasting methods to BDA; previous works have described technologies, types of analytics, and forecasting methods separately. This paper, in contrast, combines insights and provides advice on how enterprises can employ BDA in their operational, tactical, or strategic demand plans.

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Very short-term forecasting of power demand of big dynamics objects

E3S Web of Conferences ◽

10.1051/e3sconf/20198401007 ◽

2019 ◽

Vol 84 ◽

pp. 01007

Author(s):

Mirosław Parol ◽

Paweł Piotrowski ◽

Mariusz Piotrowski

Keyword(s):

Low Voltage ◽

Renewable Energy Sources ◽

Demand Forecasting ◽

Power Demand ◽

Short Term ◽

Forecasting Methods ◽

Ex Post ◽

The Subject ◽

Short Term Forecasting ◽

Micro Systems

The issue of very short-term forecasting is gaining more and more importance. It covers both the subject of power demand forecasting and forecasting of power generated in renewable energy sources. In particular, for the reason of necessity of ensuring reliable electricity supplies to consumers, it is very important in small energy micro-systems, which are commonly called microgrids. Statistical analysis of data for a sample big dynamics low voltage object will be presented in this paper. The object, in paper author’s opinion, belongs to a class of objects with difficulties in forecasting, in case of very short-term horizon. Moreover, forecasting methods, which can be applied to this type of forecasts, will be shortly characterized. Then results of sample very short-term ex post forecasts of power demand provided by several selected forecasting methods will be presented, as well as some qualitative analysis of obtained forecasts will be carried out. At the end of the paper observations and conclusions concerning analyzed subject, i.e. very short-term forecasting of power demand of big dynamics objects, will be presented.

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Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities

Journal Of Big Data ◽

10.1186/s40537-020-00329-2 ◽

2020 ◽

Vol 7 (1) ◽

Cited By ~ 1

Author(s):

Mahya Seyedan ◽

Fereshteh Mafakheri

Keyword(s):

Big Data ◽

Supply Chain ◽

Data Analytics ◽

Demand Forecasting ◽

Big Data Analytics ◽

Research Opportunities ◽

Forecasting Methods

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Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities

Sustainability ◽

10.3390/su11040987 ◽

2019 ◽

Vol 11 (4) ◽

pp. 987 ◽

Cited By ~ 26

Author(s):

Sana Mujeeb ◽

Nadeem Javaid ◽

Manzoor Ilahi ◽

Zahid Wadud ◽

Farruh Ishmanov ◽

...

Keyword(s):

Neural Network ◽

Big Data ◽

Electricity Market ◽

Short Term Memory ◽

Load Forecasting ◽

Energy Market ◽

Short Term ◽

Term Memory ◽

Forecasting Methods ◽

Long Short Term Memory

This paper focuses on analytics of an extremely large dataset of smart grid electricity price and load, which is difficult to process with conventional computational models. These data are known as energy big data. The analysis of big data divulges the deeper insights that help experts in the improvement of smart grid’s (SG) operations. Processing and extracting of meaningful information from data is a challenging task. Electricity load and price are the most influential factors in the electricity market. For improving reliability, control and management of electricity market operations, an exact estimate of the day ahead load is a substantial requirement. Energy market trade is based on price. Accurate price forecast enables energy market participants to make effective and most profitable bidding strategies. This paper proposes a deep learning-based model for the forecast of price and demand for big data using Deep Long Short-Term Memory (DLSTM). Due to the adaptive and automatic feature learning mechanism of Deep Neural Network (DNN), the processing of big data is easier with LSTM as compared to the purely data-driven methods. The proposed model was evaluated using well-known real electricity markets’ data. In this study, day and week ahead forecasting experiments were conducted for all months. Forecast performance was assessed using Mean Absolute Error (MAE) and Normalized Root Mean Square Error (NRMSE). The proposed Deep LSTM (DLSTM) method was compared to traditional Artificial Neural Network (ANN) time series forecasting methods, i.e., Nonlinear Autoregressive network with Exogenous variables (NARX) and Extreme Learning Machine (ELM). DLSTM outperformed the compared forecasting methods in terms of accuracy. Experimental results prove the efficiency of the proposed method for electricity price and load forecasting.

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