Code Monkey home page Code Monkey logo

baozhang's Introduction

#baozhang

Abstract about the paper and project

The direct-purchasing electricity policy is an attempt to reform the existing power sales mechanism, which is part of the China energy supply reform. The aim is to break the pattern of the monopoly pricing of power grid enterprises and promote the establishment of an open electricity market. This policy allows enterprises with a high electricity demand (such as heavy industry, telecommunications industry, etc.) skip the grid electricity trading platform and directly buying cheaper electricity from power plants. Every month, these enterprises give a prediction number of the electricity they plan to used next month, the power plants produce and sell the corresponding number of electricity with cheaper price to these enterprises, in this process, if the prediction number and the actual electricity consumed number has a number difference with more than plus or minus 2.5%, the enterprise will be punished. So it’s necessary to choose a reliable forecasting method to predict the future electricity consumed data accurately. Based on the historical electricity data of a telecom company from 2014 to 2017 as the training set, this paper establish a system of data forecast model, which selects the ARIMA model and Holt-Winters model as prediction model, then designing a direct-purchasing prediction system, and use this system to predict the future consumption of electricity, finally the paper evaluates the performance of the prediction accuracy of these 2 different model, it comes out that Holt-Winters model has a better performance on the data prediction base on this problem, but still can’t reach the target of lower than 2.5% differences. This paper further analyzes the reason of the error and proposes some possible solutions to reduce the prediction error, also point out some possible further enhancement of the prediction model in the future.

Demo image of the website

View Data

Financial Reporting

Financial Reporting

Analysis Data

Row Data

Cleaned Data

Prediction result for ARIMA model

Prediction result for Holt-Winters model

Comparison of two models

We can see that the Holt-winters model did better than the ARIMA model in this case.

Differrence report for the actual data and the Holt-Winters

baozhang's People

Contributors

guohm avatar

Stargazers

 avatar

Watchers

 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.