ELECTRICITY CONSUMPTION DEMAND ANALYSIS USING MACHINE LEARNING ON RANDOM FOREST ALGORITHM

Abstract

Electricity is one of the most widely used forms of energy that plays a significant part in sufficing the fundamental energy demand based on contemporary human needs. Load forecasting is used to control several operations and decisions such as dispatch, unit commitment, fuel allocation, and off-line network analysis. Forecasting consumer demand is a key factor in efficient planning of power system. The proposed method, investigated the effect of smart appliances on electric bills of a household. The proposed method effectively regulates the household loads and keeps the total electrical energy consumption below a certain threshold value.

Specification

Description:Technical field

The technical field of invention for "Predictive Electricity Consumption Demand Analysis Using Machine Learning with Random Forest Algorithm" lies within the domain of energy management, predictive analytics, and machine learning applications in the electrical engineering sector.

Background

The primary objective of "Predictive Electricity Consumption Demand Analysis Using Machine Learning with Random Forest Algorithm" is to develop a robust methodology and system for accurately forecasting electricity consumption demand. Key objectives include:
Enhanced Predictive Accuracy: Develop a machine learning-based predictive model that leverages the Random Forest algorithm to improve the accuracy of electricity consumption demand forecasts. By analysing historical consumption data alongside relevant contextual factors, the invention aims to generate precise predictions for future demand patterns.
Real-time Decision Support: Provide energy providers, grid operators, and policymakers with real-time insights into electricity consumption trends. The invention seeks to enable timely decision-making regarding energy resource allocation, infrastructure planning, and demand-side management strategies.
Scalability and Efficiency: Design an efficient and scalable system capable of handling large volumes of data while maintaining computational efficiency. By employing machine learning algorithms, particularly Random Forest, the invention aims to process extensive datasets and generate predictions in a timely manner.
Adaptability to Varied Contexts: Develop a methodology that can accommodate diverse contextual factors influencing electricity consumption, such as weather patterns, time of day, and day of the week, holidays, and special events. The invention seeks to incorporate these factors into the predictive model to enhance its accuracy and applicability across different regions and scenarios.
Integration with Smart Grid Technologies: Facilitate seamless integration with smart grid infrastructure to enable real-time monitoring, control, and optimization of electricity consumption. The invention aims to support the transition towards more intelligent and efficient energy distribution systems by providing actionable insights derived from predictive analytics.
Support for Sustainable Energy Practices: Enable the integration of renewable energy sources into the grid by forecasting electricity demand patterns and identifying opportunities for optimization. By promoting energy efficiency and demand-side management, the invention contributes to the transition towards a more sustainable and resilient energy ecosystem.

Summary of the Invention

The "Predictive Electricity Consumption Demand Analysis Using Machine Learning with Random Forest Algorithm" revolutionizes the way electricity consumption demand is analysed and predicted. By combining advanced machine learning techniques with real-time data analytics, this invention empowers stakeholders in the energy sector to make informed decisions and optimize the efficiency, reliability, and sustainability of electricity distribution systems.
Detailed Description of the Invention:
Forecaster Web App:
The Forecaster Web App serves as the central interface for users to access and interact with the predictive electricity consumption demand analysis system. It provides a user-friendly platform for inputting data, viewing predictions, and accessing analysis tools.
Smart Metered Dataset Annotation:
Training Dataset:This component involves the annotation and preparation of a training dataset comprised of historical electricity consumption data collected from smart meters. The dataset is labeled with corresponding contextual factors such as weather conditions, time of day, and holidays to facilitate machine learning model training.
Testing Data:
Similarly, testing data is prepared to evaluate the performance of the predictive model. This dataset contains unseen instances of electricity consumption data along with contextual factors for validation and testing purposes.
Pre-processing:
The pre-processing step involves cleaning and transforming the raw electricity consumption data and contextual factors to prepare them for analysis. This may include handling missing values, normalization, and feature engineering to extract relevant information for model training.
K-means Clustering:
K-means clustering is employed to segment the dataset into clusters based on similarities in electricity consumption patterns. This clustering technique helps identify distinct consumption behaviors and can improve the accuracy of the predictive model by capturing the variability in consumption demand across different segments.
Fast Fourier Transform Feature Extraction:
Fast Fourier Transform (FFT) is utilized to extract frequency-domain features from the electricity consumption time series data. This feature extraction technique transforms the data into the frequency domain, allowing the identification of periodic consumption patterns and trends that may influence demand.
HSBUFC Classification:
HSBUFC (Hierarchical Support-Based Unsupervised Fuzzy C-Means) classification is applied to classify electricity consumption demand patterns into different categories. This classification method utilizes a hierarchical approach to cluster similar consumption patterns and assign fuzzy memberships to each pattern, enabling more granular analysis and prediction.
Prediction:
using the Random Forest algorithm, the system generates predictions for future electricity consumption demand based on the processed data and extracted features. The trained model takes into account historical consumption patterns, contextual factors, and clustering results to forecast demand with high accuracy.
Performance Analysis:
Performance analysis involves evaluating the accuracy and effectiveness of the predictive model. Metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and coefficient of determination (R-squared) are calculated to assess the model's predictive power and identify areas for improvement.
User Interface:
Consumer Login:
the user interface includes a consumer login feature that allows consumers to access personalized electricity consumption predictions and analysis tools. Consumers can input their historical consumption data and view predictions tailored to their specific usage patterns, enabling them to make informed decisions about energy usage and conservation strategies.
, Claims:1. Receiving historical electricity consumption data. Collecting contextual factorsinfluencing electricity consumption. Pre-processing the data to extract relevant features. Training a Random Forest algorithm using the pre-processed data.
2. A pre-processing module configured to pre-process the received data and extract relevant features.A Random Forest algorithm module configured to train a predictive model using the pre-processed data.prediction module configured to predict future electricity consumption demand based on the trained model; and
3. An output module configured to provide the predicted demand to users.Generating a predictive model for electricity consumption demand based on the trained algorithm.
4. A computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform a method for predicting electricity consumption demand

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