1.1 Introduction
Machine learning (ML) is the study of computer algorithms that can improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks (Wikipedia, 2021).
As a prelude to other parts of this study, this chapter will discuss the background upon which this study was initiated, the statement of problems that led to this study, the Aim and Objectives of the study. Others are Significance of the study, Scope of work, Limitations of the Study and Definition of technical terms.
1.2 Background of Study
According to EIA (2021), Energy consumption patterns have changed over the history of our country as we developed new energy sources and as our uses of energy changed. Wood (a renewable energy source) served as the preeminent form of energy until the mid- to late-1800s, even though water mills were important to some early industrial growth. Coal became dominant in the late 19th century before being overtaken by petroleum products in the middle of the last century, a time when natural gas usage also rose quickly (EIA, 2021).
Since the mid 20th century, usage of coal has again increased (mainly as a primary energy source for electric power generation), and a new form of energy–nuclear electric power–has made an increasingly significant contribution. After a pause in the 1970s, the use of petroleum and natural gas resumed growth, and the overall pattern of energy usage since the late 20th century has remained fairly stable (EIA, 2021).
A subset of machine learning is closely related to computational statistics, which focuses on making predictions using computers; but not all machine learning is statistical learning. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning. Some implementations of machine learning use data and neural networks in a way that mimics the working of a biological brain. In its application across business problems, machine learning is also referred to as predictive analytics (Wikipedia, 2021).
Therefore, in Nigeria where the research was carried out, the activities that was conducted is to know the Evaluation of a Machine Learning Approach on Energy Consumption.
1.3 Statement of Problems
Investigation reveals the problems of the Evaluation of a Machine Learning Approach on Energy Consumption research work, which includes the following:
- Understanding Which Processes Need Automation: It's becoming increasingly difficult to separate fact from fiction in terms of Machine Learning today. Before you decide on which AI platform to use, you need to evaluate which problems you’re seeking to solve. The easiest processes to automate are the ones that are done manually every day with no variable output. Complicated processes require further inspection before automation. While Machine Learning can definitely help automate some processes, not all automation problems need Machine Learning (Provintl, 2021).
- Lack of Quality Data: The number one problem facing Machine Learning is the lack of good data. While enhancing algorithms often consumes most of the time of developers in AI, data quality is essential for the algorithms to function as intended. Noisy data, dirty data, and incomplete data are the quintessential enemies of ideal Machine Learning. The solution to this conundrum is to take the time to evaluate and scope data with meticulous data governance, data integration, and data exploration until you get clear data. You should do this before you start (Provintl, 2021).
- Inadequate Infrastructure: Machine Learning requires vast amounts of data churning capabilities. Legacy systems often can’t handle the workload and buckle under pressure. You should check if your infrastructure can handle Machine Learning. If it can’t, you should look to upgrade, complete with hardware acceleration and flexible storage (Provintl, 2021).
- Implementation: Organizations often have analytics engines working with them by the time they choose to upgrade to Machine Learning. Integrating newer Machine Learning methodologies into existing methodologies is a complicated task. Maintaining proper interpretation and documentation goes a long way to easing implementation. Partnering with an implementation partner can make the implementation of services like anomaly detection, predictive analysis, and ensemble modeling much easier (Provintl, 2021).
- Lack of Skilled Resources: Deep analytics and Machine Learning in their current forms are still new technologies. Thus, there is a shortage of skilled employees available to manage and develop analytical content for Machine Learning. Data scientists often need a combination of domain experience as well as in-depth knowledge of science, technology, and mathematics. Recruitment will require you to pay large salaries as these employees are often in high-demand and know their worth. You can also approach your vendor for staffing help as many managed service providers keep a list of skilled data scientists to deploy anytime (Provintl, 2021).
1.4 Aim and Objectives of Study
The aim of the study is to evaluate the machine learning approach on energy consumption. In achieving this aim, the following specific objectives were set out as follows:
- To discover patterns in the user data and then make predictions based on these and intricate patterns for answering business questions and solving business problems.
- To analyze data as well as identifying trends on Machine learning approach on energy consumption.
1.5 Research Questions
The following questions will be addressed to evaluate the machine learning approach on energy consumption:
- Does machine learning approach enhance energy consumption?
- What is the approach used in machine learning to evaluate energy consumption?
- Is there any significant impact of machine learning approach on energy consumption?
1.6 Research Hypothesis
In order to pursue the objective of this study, the following generalized statements have been designed to guide and aids in obtaining the result for the experiment to be conducted. For this work, the null hypothesis will be represented with H0 while the alternative hypothesis will be represented with hypothesis H1.
- H0: There is no significant impact of machine learning approach on energy consumption.
- H1: There is a significant impact of machine learning approach on energy consumption.
1.7 Significance of Study
This study will be of immense benefit to other researchers who intend to know more on this study and can also be used by non-researchers to build more on their research work. This study contributes to knowledge and could serve as a guide for other study.
1.8 Scope of the Study
The study focuses on the Evaluation of Machine Learning Approach on Energy Consumption
1.9 Limitations of the Study
During the course of this study, many things militated against its completion, some of which are:
- Time Constraint: The time frame given to accomplish this project was very short due to school academic calendar and it was carried out under pressure which made the researcher not to implement some necessary features.
- Establishment Policies: Establishment policies posed a serious limitation as most staffs are not ready to release information needed for this project work.
- Research material: availability of research material is a major setback to the scope of the study.
- Frequent power failure: This made the researcher append more money on fuel to ensure sustainable power.
- Financial Constraint: Insufficient fund tends to impede the efficiency of the researcher in sourcing for the relevant materials, literature or information and in the process of data collection (internet, questionnaire and interview).
