Demand side power load forecasting (Matlab code implementation)

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本文目录如下：️️️

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目录

1 概述

2 数学模型

3 运行结果 

4 MatlabCode and articles are explained in detail 

5 写在最后

1 概述

Building electricity demand forecasting is bound to play an important role in the future power grid.Given the deployment of intermittent renewable energy sources and increasing electricity consumption,Generating accurate building-level electricity demand forecasts will be valuable to both grid operators and building energy management systems.The literature contains a wealth of predictive models for individual buildings.然而,An ongoing challenge is to develop a broadly applicable approach,Used across geographies、Demand forecast by season and type of use.This paper addresses the need for a general approach to electricity demand forecasting,This method works by formulating an ensemble learning method,Perform model validation and selection in real-time using gating functions.By learning from electricity demand data streams,The approach requires little knowledge of the energy end use,So it is very suitable for practical deployment.While ensemble methods are able to incorporate complex predictors,Such as artificial neural networks or seasonal autoregressive ensemble moving average models,But this work will focus on adopting simpler models,For example Ordinary Least Squares Sum k-最近邻.By applying our method to 32 A building power demand dataset（8 commercial and 24 个住宅）,We generated mean absolute percentage errors for commercial and residential buildings, respectively 7.5% 和 55.8% electricity demand forecast.Improves the accuracy of electricity demand forecasts and facilitates power system management,Recent attention has been paid to short-term building-level electricity demand forecasting using various models [4][5].The ability to accurately and adaptively forecast demand-side loads will play a key role in maintaining grid stability and enabling renewable energy integration.此外,Under the research framework of demand response and microgrid management,Many novel optimal control schemes require short-term building power demand forecasts to aid decision-making[6].

2 数学模型

详细数学模型见第4部分.

3 运行结果 

Here are the results of two days of running,The remaining five days will not be displayed one by one. 

4 MatlabCode and articles are explained in detail 

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5 写在最后

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