Short Term Electricity Load Forecasting Through Machine Learning

Essentially, Electricity Load Forecasting is an approximation of upcoming active loads from a variety of load buses before the active loads occur. Also, it is an important factor for power system energy management. Accurate and precise load forecasting can help to reduce the capacity of the power system, to make unit commitment decisions, and to increase the dependability of power systems. Hence, this paper presents a generalized regression Neural Network (GRNN) based approach for Short Term Load Forecasting (STLF). The results showed that the performance of GRNN with 30 neurons is better of short-term load forecasting in comparison with 10 neurons. For 10 neurons, the Mean Absolute Percentage Error (MAPE) was 2.10% and Mean Absolute Error (MAE) was 306.21 MWh. However, for 30 neurons it was observed that MAPE is 1.81% and MAE 268.48 MWh.

[1]  Sreenatha G. Anavatti,et al.  Applications of General Regression Neural Networks in Dynamic Systems , 2018, Digital Systems.

[2]  Aboul Ella Hassanien,et al.  Feature selection via a novel chaotic crow search algorithm , 2017, Neural Computing and Applications.

[3]  Ahmad Taher Azar,et al.  Hybrid Tolerance Rough Set: PSO Based Supervised Feature Selection for Digital Mammogram Images , 2013, Int. J. Fuzzy Syst. Appl..

[4]  Vibhor Kant,et al.  A hybrid method based on neural network and improved environmental adaptation method using Controlled Gaussian Mutation with real parameter for short-term load forecasting , 2019, Energy.

[5]  Nikos D. Hatziargyriou,et al.  A novel fuzzy-based ensemble model for load forecasting using hybrid deep neural networks , 2020 .

[6]  Danladi Ali,et al.  Application of fuzzy – Neuro to model weather parameter variability impacts on electrical load based on long-term forecasting , 2017 .

[7]  Ahmad Taher Azar,et al.  Fuzzy firefly clustering for tumour and cancer analysis , 2017 .

[8]  Aboul Ella Hassanien,et al.  Adaptive k-means clustering algorithm for MR breast image segmentation , 2013, Neural Computing and Applications.

[9]  Aboul Ella Hassanien,et al.  A random forest classifier for lymph diseases , 2014, Comput. Methods Programs Biomed..

[10]  Niklas Panten,et al.  Very short-term load forecasting on factory level – A machine learning approach , 2019, Procedia CIRP.

[11]  Chih-Chou Chiu,et al.  Combining a neural network and a rule-based expert system for short-term load forecasting , 1997 .

[12]  Jing Zhao,et al.  A hybrid method of dynamic cooling and heating load forecasting for office buildings based on artificial intelligence and regression analysis , 2018, Energy and Buildings.

[13]  Aboul Ella Hassanien,et al.  MRI breast cancer diagnosis hybrid approach using adaptive ant-based segmentation and multilayer perceptron neural networks classifier , 2014, Appl. Soft Comput..

[14]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[15]  Ahmad Taher Azar,et al.  Rough set theory with Jaya optimization for acute lymphoblastic leukemia classification , 2019, Neural Computing and Applications.

[16]  Ahmad Taher Azar,et al.  Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis , 2014, Comput. Methods Programs Biomed..

[17]  Sreenatha G. Anavatti,et al.  Altitude identification and intelligent control of a flapping wing micro aerial vehicle using modified generalized regression neural networks , 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).

[18]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[19]  H. Madsen,et al.  Long-term electricity load forecasting: Current and future trends , 2019, Utilities Policy.

[20]  Ahmad Taher Azar,et al.  Covering-based rough set classification system , 2016, Neural Computing and Applications.

[21]  Cláudia Neves,et al.  Short Term Load Forecasting , 2019, Forecasting and Assessing Risk of Individual Electricity Peaks.

[22]  Aboul Ella Hassanien,et al.  Hybrid rough-bijective soft set classification system , 2016, Neural Computing and Applications.

[23]  George G. Karady,et al.  Fuzzy logic for short term load forecasting , 1996 .

[24]  Wei Zhong,et al.  Short-term Electric Load Forecasting Based on Wavelet Neural Network, Particle Swarm Optimization and Ensemble Empirical Mode Decomposition , 2017 .

[25]  Wei-Chiang Hong,et al.  Short term load forecasting based on phase space reconstruction algorithm and bi-square kernel regression model , 2018, Applied Energy.

[26]  S. A. Soliman,et al.  Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model , 2004 .

[27]  Ahmad Taher Azar,et al.  Tolerance rough set firefly-based quick reduct , 2016, Neural Computing and Applications.

[28]  Ahmad Taher Azar,et al.  Improved dominance rough set-based classification system , 2017, Neural Computing and Applications.

[29]  Shereen M. El-Metwally,et al.  Decision tree classifiers for automated medical diagnosis , 2013, Neural Computing and Applications.

[30]  Ahmad Taher Azar,et al.  Feature selection using swarm-based relative reduct technique for fetal heart rate , 2014, Neural Computing and Applications.

[31]  Abdul Hanan Abdullah,et al.  Short-term load forecasting using a hybrid model with a refined exponentially weighted fuzzy time series and an improved harmony search , 2014 .

[32]  Abbas Khosravi,et al.  A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings , 2015 .

[33]  Chan-Nan Lu,et al.  Two-Stage Artificial Neural Network Model for Short-Term Load Forecasting , 2018 .

[34]  W. R. Christiaanse.  Short-Term Load Forecasting Using General Exponential Smoothing , 1971 .

[35]  Xuan Yang,et al.  Short-term electricity load forecasting based on feature selection and Least Squares Support Vector Machines , 2019, Knowl. Based Syst..