Online ISSN: 2515-8260

Keywords : forecasting

Minimising The Estimation Error Of Forecasting The Electricity Consumption In Malaysia

Norazliani MD LAZAM; Nur Izzati SHARIL; Suraya MOHD; Norsyafika Azwa MOHD SHARIFF; Nur Farah Haifa MD KAMAL

European Journal of Molecular & Clinical Medicine, 2021, Volume 8, Issue 2, Pages 159-169

This paper presents a study on minimising the estimation error of forecasting the
electricity consumption in Malaysia. A robust and accurate forecasts of electricity
consumption are deemed crucial for the supplier to arrive on fair estimations of electricity
supply optimally. Thus, identifying the best model to forecast the electricity consumption
accurately may hinder energy wastage. This research aims to examine which model gives
the least error in estimating the future electricity consumptions in Malaysia. Two models
were tested namely Artificial Neural Network (ANN) and Regression Methods. In analysing
these models, this research applies the Microsoft Excel and SAS Enterprise Miner (SAS)
software. The data were extracted from the Department of Statistics Malaysia (DOSM),
CEIC Data Company and The Statistics Portal. Results indicate that ANN produces least
error as compared to the Regression Method as the former fits the data well whilst the latter
overfits the data. The ANN model uses NNTool from MATLAB is used for forecasting
future electricity consumption. The forecasted values (2020-2022) proved to provide more
interpretable forecasts. This study may benefit the electricity supplier, consumers and also
the Government of Malaysia, in particular the Ministry of Energy and Natural Resources.
It may provide insights on estimating the optimum amount of energy to be generated. This
will definitely increase the savings and reduce wastage from every angle. Ultimately, the
environment is saved too.



European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 3, Pages 2271-2285

With the rapid growth of COVID-19 pandemic infectious disease caused by the Corona Virus. It was first identified in Wuhan in December 2019. It expanded its circle all over the world and finally spreading its route to India. The whole world is fighting against the spread of this deadly disease, cases in India also gradually increasing day by day since May after lockdown. This article proposes how to contribute to utilizing the machine learning and deep learning models with the aim for understanding its everyday exponential behaviour along with the prediction of future reachability of the COVID-2019 across the nations by utilizing the real-time information from the Johns Hopkins. This paper studies the COVID-19 dataset and explore the data by data visualization with different libraries that are available in Python. The paper also discusses the current situation in India while tackling the Covid-19 pandemic and the ongoing development in AI and ML has significantly improved treatment, medication, screening tests, prediction, forecasting, contact tracing, and drug/vaccine development process for the Covid19 pandemic and reduce the human intervention in medical practice. However, most of the models are not deployed enough to show their real-world operation, but they are still up to the mark. Within this paper, we present Exploratory Data Analysis, Data Preprocessing, Data Cleaning and Manipulations, Machine Learning Algorithms, Pandemic Analyzing Engine GUI, and Deep Learning. We have performed linear regression, Decision Tree, SVM, Random Forest and for forecasting, we performed FBPrompet, ARIMA model to predict the next 15 day’s Pandemic situation.