IDARE Enterprise AI predictive analytics platform background

Use Case

Predicting Solar Power Generated in a Plant

Forecasting the total production of solar energy in a plant is essential to meet demand and supply.

1Overview & Strategic Importance

Predicting Solar Power Generated in a Plant

Regression Solution Renewable Energy

Problem Statement

Predicting solar energy production is essential for optimizing renewable energy utilization, improving grid stability, and ensuring efficient energy planning. Forecasting is challenging due to environmental factors such as cloud cover, temperature, humidity, and wind speed. Addressing this enhances energy efficiency and supports sustainable management.

Required Solutions

Developing ML models for energy output forecasting.
Identifying key influences like cloud cover and pressure.
Optimizing resource allocation for grid stability.

Solution Objectives

Identify patterns in production data via EDA.
Develop accurate regression models for weather conditions.
Conduct scenario analysis for generation efficiency.
Support real-world forecasting and sustainable planning.

Understanding the Problem

Energy output is determined by radiation, cloud cover, and humidity.

AI models analyze historical patterns to predict output accurately, enabling data-driven decisions to maximize renewable energy utilization.

2About the Data

Data Collection

The analysis uses the Solar Prediction Dataset, including features such as temperature, humidity, and solar radiation intensity.

Dataset Source

Major Parameters Description

Download Training Data

Timestamp

Recorded date and time of the observation.

Temp

Temperature in degrees Celsius or Fahrenheit.

Chill

Wind chill temperature.

HIndex

Heat index considering temperature and humidity.

Humid

Relative humidity percentage.

Dewpt

Dew point temperature.

Wind

Wind speed recorded.

HiWind

Highest recorded wind speed.

WindDir

Wind direction in degrees (0-360°).

3Using iDareAI

Guided Mode Initialization

AUploading Dataset

Click on the **'Upload CSV or Excel Data'** button → Select a source for the dataset → Upload `solar_prediction_train.csv`. The system automatically analyzes the file, extracts column descriptions, and identifies the top value-adding targets for prediction.

BChoosing Analysis Mode

Choose between autonomous machine learning or manual building. In autonomous mode, ask questions such as:

What factors affect the amount of solar energy produced?
Can the solar energy production be maximized using this data?

Operation Using Autonomous Guided Mode

AQuery Response

The factors include temperature, humidity, and cloud coverage. Random Forest produced the most reliable predictions (approx. 380.57 units). It is usable with caution for practical applications.

BAI Application

Model Fine-Tuning/Manual Model Building

ASelecting Prediction Target

'Solar' column was selected as the target.

BSelecting Analysis Type

The analysis target is a numeric column. Hence, the 'Regression' analysis type is selected.

CSelecting Model Group/Item

DSelecting Features

Selected Humid, Dewpt, Wind, HiWind, Barom, ET, and Day.

ESelecting Training Level

Moderate training level with 5-fold cross-validation.

AI Modeling Details

Linear Regression emerged as the best baseline model (20.29% test error).

Training Analysis Details

APredicted Solar

BPredicted Trend

CError Trend

DFeature Importance

Improving Model Accuracy

ACustom Variable Creation

Derived features such as 'Day of Year' and 'Solar Radiation Index' capture complex environmental patterns.

Variable Name	Formula
Day of the Year	TEXT(A2, "ddd")
Week of the Year	WEEKNUM(A2)
Month	MONTH(A2)
Season	IF(OR(MONTH(A2)=12, MONTH(A2)<=2), "Winter", ...)
Dew Point Depression	D2 - E2

BAnalysis Details (Improved)

Accuracy increased to 90.9% using targeted feature engineering.

4AI APPLICATION

Manual Model Building

In manual mode, users can modify sliders for variables like Humidity, Dew Point, and Wind Speed to see real-time impact on solar radiation intensity.

AI Application Demo

Adjusting ET and Humidity sliders updates the query response to reflect their influence on solar energy output.

Saving the Project

Save your solar production analysis by clicking the icon at the bottom left corner of the interface.

Sharing the Project

Generate a shareable link once the project is saved for collaborative energy forecasting.

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