Impact Study of Temperature on the Time Series Electricity Demand of Urban Nepal for Short-Term Load Forecasting

Short-term electricity demand forecasting is one of the best ways to understand the changing characteristics of demand that helps to make important decisions regarding load flow analysis, preventing imbalance in generation planning, demand management, and load scheduling, all of which are actions for the reliability and quality of that power system. The variation in electricity demand depends upon various parameters, such as the effect of the temperature, social activities, holidays, the working environment, and so on. The selection of improper forecasting methods and data can lead to huge variations and mislead the power system operators. This paper presents a study of electricity demand and its relation to the previous day’s lags and temperature by examining the case of a consumer distribution center in urban Nepal. The effect of the temperature on load, load variation on weekends and weekdays, and the effect of load lags on the load demand are thoroughly discussed. Based on the analysis conducted on the data, short-term load forecasting is conducted for weekdays and weekends by using the previous day’s demand and temperature data for the whole year. Using the conventional time series model as a benchmark, an ANN model is developed to track the effect of the temperature and similar day patterns. The results show that the time series models with feedforward neural networks (FF-ANNs), in terms of the mean absolute percentage error (MAPE), performed better by 0.34% on a weekday and by 8.04% on a weekend.

[1]  Ali Ouni,et al.  Single and Multi-Sequence Deep Learning Models for Short and Medium Term Electric Load Forecasting , 2019, Energies.

[2]  Somsak Kittipiyakul,et al.  Performance Analysis of Short-Term Electricity Demand with Atmospheric Variables , 2018 .

[3]  Zhongyi Hu,et al.  Comprehensive learning particle swarm optimization based memetic algorithm for model selection in short-term load forecasting using support vector regression , 2014, Appl. Soft Comput..

[4]  Arunesh Kumar Singh,et al.  Load forecasting techniques and methodologies: A review , 2012, 2012 2nd International Conference on Power, Control and Embedded Systems.

[5]  K. Chapagain,et al.  Short-Term Electricity Demand Forecasting: Impact Analysis of Temperature for Thailand , 2020, Energies.

[6]  S. Mirasgedis,et al.  Modeling framework for estimating impacts of climate change on electricity demand at regional level: Case of Greece , 2007 .

[7]  Eklas Hossain,et al.  A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models , 2020, IEEE Access.

[8]  Chun-Ho Cho,et al.  The Estimation of Base Temperature for Heating and Cooling Degree-Days for South Korea , 2014 .

[9]  Yong Cheol Kang,et al.  Short-term load forecasting for special days in anomalous load conditions using neural networks and fuzzy inference method , 2000 .

[10]  Wattanapong Rakwichian,et al.  The impacts of climatic and economic factors on residential electricity consumption of Bangkok Metropolis , 2008 .

[11]  W. Y. Fung,et al.  Impact of urban temperature on energy consumption of Hong Kong , 2006 .

[12]  R. Ramanathan,et al.  Short-run forecasts of electricity loads and peaks , 1997 .

[13]  A. C. Liew,et al.  Demand forecasting using fuzzy neural computation, with special emphasis on weekend and public holiday forecasting , 1995 .

[14]  Zili Li,et al.  Forecasting day-ahead electricity load using a multiple equation time series approach , 2016, Eur. J. Oper. Res..

[15]  Shree Raj Shakya,et al.  Short Term Electric Load Forecasting of Kathmandu Valley of Nepal using Artificial Neural Network , 2018 .

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

[17]  Bin Li,et al.  A Weekend Load Forecasting Model Based on Semi-Parametric Regression Analysis Considering Weather and Load Interaction , 2019, Energies.

[18]  Andre Luis Santiago Maia,et al.  Holt’s exponential smoothing and neural network models for forecasting interval-valued time series , 2011 .

[19]  Kuruge Darshana Abeyrathna,et al.  Hybrid Particle Swarm Optimization With Genetic Algorithm to Train Artificial Neural Networks for Short-Term Load Forecasting , 2019, Int. J. Swarm Intell. Res..

[20]  Francisco Gonzalez-Longatt,et al.  A Bayesian Model to Forecast the Time Series Kinetic Energy Data for a Power System , 2021 .

[21]  Yan Quan Liu,et al.  Forecasting of Chinese Primary Energy Consumption in 2021 with GRU Artificial Neural Network , 2017 .

[22]  Ali Ouni,et al.  Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches † , 2018, Energies.

[23]  Nahid-Al-Masood,et al.  Temperature sensitivity forecasting of electrical load , 2010, 2010 4th International Power Engineering and Optimization Conference (PEOCO).

[24]  Farshid Keynia,et al.  Mid-term load forecasting of power systems by a new prediction method , 2008 .

[25]  Georgios Giasemidis,et al.  Short term load forecasting and the effect of temperature at the low voltage level , 2019, International Journal of Forecasting.

[26]  Shahaboddin Shamshirband,et al.  Computational Intelligence on Short-Term Load Forecasting: A Methodological Overview , 2019, Energies.

[27]  Hua Liao,et al.  Climate impacts: temperature and electricity consumption , 2019, Natural Hazards.

[28]  Ashish Shrestha,et al.  Performance Enhancement of Radial Distribution System via Network Reconfiguration: A Case Study of Urban City in Nepal , 2021 .