Short Term Load Forecasts of Low Voltage Demand and the Effects of Weather

Short term load forecasts will play a key role in the implementation of smart electricity grids. They are required to optimise a wide range of potential network solutions on the low voltage (LV) grid, including integrating low carbon technologies (such as photovoltaics) and utilising battery storage devices. Despite the need for accurate LV level load forecasts, previous studies have mostly focused on forecasting at the individual household or building level using data from smart meters. In this study we provide detailed analysis of a variety of methods in terms of both point and probabilistic forecasting accuracy using data from 100 real LV feeders. Moreover, we investigate the effect of temperature (both actual and forecasts) on the accuracy of load forecasts. We present some important results on the drivers of LV forecasting accuracy that are crucial for the management of LV networks, along with an empirical comparison of forecast measures. 1 ar X iv :1 80 4. 02 95 5v 1 [ st at .A P] 6 A pr 2 01 8

[1]  Georgios Giasemidis,et al.  A hybrid model of kernel density estimation and quantile regression for GEFCom2014 probabilistic load forecasting , 2016, 1610.05183.

[2]  Jooyoung Jeon,et al.  Using Conditional Kernel Density Estimation for Wind Power Density Forecasting , 2012 .

[3]  M. Etezadi-Amoli,et al.  Smart meter based short-term load forecasting for residential customers , 2011, 2011 North American Power Symposium.

[4]  Gabriel H. Tucci,et al.  Analysis of the residential, commercial and industrial electricity consumption , 2011, 2011 IEEE PES Innovative Smart Grid Technologies.

[5]  Peter B. Luh,et al.  An Efficient Approach to Short-Term Load Forecasting at the Distribution Level , 2016, IEEE Transactions on Power Systems.

[6]  Hamid Shaker,et al.  Short-term electricity load forecasting of buildings in microgrids , 2015 .

[7]  Junwei Lu,et al.  Autoregressive with Exogenous Variables and Neural Network Short-Term Load Forecast Models for Residential Low Voltage Distribution Networks , 2014 .

[8]  G. Kariniotakis,et al.  Probabilistic day-ahead forecasting of household electricity demand , 2017 .

[9]  Hesham K. Alfares,et al.  Electric load forecasting: Literature survey and classification of methods , 2002, Int. J. Syst. Sci..

[10]  R. Koenker,et al.  Regression Quantiles , 2007 .

[11]  Gabriela Hug,et al.  Forecasting of Smart Meter Time Series Based on Neural Networks , 2016, DARE@PKDD/ECML.

[12]  Peter Tzscheutschler,et al.  Short-term smart learning electrical load prediction algorithm for home energy management systems , 2015 .

[13]  Heng Huang,et al.  Using Smart Meter Data to Improve the Accuracy of Intraday Load Forecasting Considering Customer Behavior Similarities , 2015, IEEE Transactions on Smart Grid.

[14]  Filippo Maria Bianchi,et al.  Local short term electricity load forecasting: Automatic approaches , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[15]  Siddharth Arora,et al.  Forecasting electricity smart meter data using conditional kernel density estimation , 2014, 1409.2856.

[16]  Tin Kam Ho,et al.  A Sparse Coding Approach to Household Electricity Demand Forecasting in Smart Grids , 2017, IEEE Transactions on Smart Grid.

[17]  Tao Hong,et al.  Comparing two model selection frameworks for probabilistic load forecasting , 2016, 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS).

[18]  J. W. Taylor,et al.  Short-term electricity demand forecasting using double seasonal exponential smoothing , 2003, J. Oper. Res. Soc..

[19]  Oleg Valgaev,et al.  Low-voltage power demand forecasting using K-nearest neighbors approach , 2016, 2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia).

[20]  Milan Prodanovic,et al.  Short-Term Load Forecasting at the local level using smart meter data , 2015, 2015 IEEE Eindhoven PowerTech.

[21]  Wei Wang,et al.  Electricity load forecasting by an improved forecast engine for building level consumers , 2017 .

[22]  Karl Aberer,et al.  Electricity load forecasting for residential customers: Exploiting aggregation and correlation between households , 2013, 2013 Sustainable Internet and ICT for Sustainability (SustainIT).

[23]  A. S. Hurn,et al.  Forecasting quantiles of day-ahead electricity load , 2017 .

[24]  A. Raftery,et al.  Strictly Proper Scoring Rules, Prediction, and Estimation , 2007 .

[25]  Hans-Arno Jacobsen,et al.  Household electricity demand forecasting: benchmarking state-of-the-art methods , 2014, e-Energy.

[26]  W. Patterson,et al.  Energy forecasting , 1977, Nature.

[27]  Magnus Dahl,et al.  Using ensemble weather predictions in district heating operation and load forecasting , 2017 .

[28]  Tao Hong,et al.  Long Term Probabilistic Load Forecasting and Normalization With Hourly Information , 2014, IEEE Transactions on Smart Grid.

[29]  Tao Hong,et al.  Probabilistic electric load forecasting: A tutorial review , 2016 .

[30]  Matthew Rowe,et al.  A Peak Reduction Scheduling Algorithm for Storage Devices on the Low Voltage Network , 2014, IEEE Transactions on Smart Grid.

[31]  Mark Last,et al.  Short-Term Load Forecasting in Smart Meters with Sliding Window-Based ARIMA Algorithms , 2017, ACIIDS.

[32]  Saifur Rahman.  Formulation and analysis of a rule-based short-term load forecasting algorithm , 1990 .

[33]  Carlos Alzate,et al.  Improved Electricity Load Forecasting via Kernel Spectral Clustering of Smart Meters , 2013, 2013 IEEE 13th International Conference on Data Mining.

[34]  Rob J. Hyndman,et al.  Hierarchical Probabilistic Forecasting of Electricity Demand With Smart Meter Data , 2020 .

[35]  Ram Rajagopal,et al.  Short Term Electricity Load Forecasting on Varying Levels of Aggregation , 2014, ArXiv.

[36]  Siddharth Arora,et al.  Rule-based autoregressive moving average models for forecasting load on special days: A case study for France , 2018, Eur. J. Oper. Res..

[37]  Matthew Rowe,et al.  The Real-Time Optimisation of DNO Owned Storage Devices on the LV Network for Peak Reduction , 2014 .

[38]  Rob J. Hyndman,et al.  Forecasting Uncertainty in Electricity Smart Meter Data by Boosting Additive Quantile Regression , 2016, IEEE Transactions on Smart Grid.

[39]  Akhtar Kalam,et al.  Electricity load forecasting for Urban area using weather forecast information , 2016, 2016 IEEE International Conference on Power and Renewable Energy (ICPRE).

[40]  Pierre Pinson,et al.  Global Energy Forecasting Competition 2012 , 2014 .

[41]  Rob J Hyndman,et al.  Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond , 2016 .

[42]  Peter Grindrod,et al.  A genetic algorithm approach for modelling low voltage network demands , 2016, 1612.06833.

[43]  Rodney Anthony Stewart,et al.  Forecasting low voltage distribution network demand profiles using a pattern recognition based expert system , 2014 .

[44]  Guillaume Foggia,et al.  Neural Network-Based Model Design for Short-Term Load Forecast in Distribution Systems , 2016, IEEE Transactions on Power Systems.

[45]  Joao M.C. Sousa,et al.  Short-term load forecasting using information obtained from low voltage load profiles , 2009, 2009 International Conference on Power Engineering, Energy and Electrical Drives.

[46]  Peter Grindrod,et al.  A new error measure for forecasts of household-level, high resolution electrical energy consumption , 2014 .

[47]  Marie Bessec,et al.  The non-linear link between electricity consumption and temperature in Europe: A threshold panel approach , 2008 .