TY  - JOUR
T1  - Electrical Load Forecasting Methodologies and Approaches
AU  - Tıwarı, Seemant
PY  - 2022
DA  - December
DO  - 10.55549/epstem.1218629
JF  - The Eurasia Proceedings of Science Technology Engineering and Mathematics
JO  - EPSTEM
PB  - ISRES Publishing
WT  - DergiPark
SN  - 2602-3199
SP  - 1
EP  - 8
VL  - 19
LA  - en
AB  - Load forecasting is indeed a strategy used mostly by power providers to predict the amount of power or energy required to match market dynamics at all moments. Electricity load prediction is a dangerous process trendy the electrical company's development besides theatres a dangerous character in electrical capacities allocation and power structure organization; as a result, it consumes increasingly gained research interest. As a result, the reliability of power demand prediction is critical for electricity resource planning and electrical management system. The increasing rise of database files in market research, together with data processing, created an urgent need development of an effective instrument process for capturing concealed and crucial understanding of load prediction from accessible enormous data sets. Many machine learning techniques, as a potential subset of computer engineering, are well suited to the answer to this issue. This text delivers an impression of authority weight prediction practices besides algorithms. Notwithstanding the complexity of all studied methods, the evaluation demonstrates that regression analysis itself is frequently utilized and economical for long-term prediction. Machine learning or artificially intelligent methods like Neural Networks, Support Vector Machines, and Fuzzy logic are ideal for short-term estimates.
KW  - Artificial Neural Networks (ANNs)
KW  - Prediction of load demand
KW  - Forecasting methods and algorithms
KW  - Time Series
CR  - Tiwari, S. (2022). Electrical load forecasting methodologies and approaches. The Eurasia Proceedings of Science, Technology, Engineering & Mathematics (EPSTEM), 19, 1-8.
UR  - https://doi.org/10.55549/epstem.1218629
L1  - https://dergipark.org.tr/en/download/article-file/2830017
ER  -