# An Artificial-Intelligence-Based Renewable Energy Prediction Program for Demand-Side Management in Smart Grids

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## Abstract

**:**

## 1. Introduction

## 2. RES Prediction

#### 2.1. Solar Power Prediction (SPP)

#### 2.1.1. Components and Applications of Irradiance

^{2}of extraterrestrial SP at the top of the atmosphere. How good a variable value depends on the local weather and where it is in ground-level SP. Global Horizontal Irradiance (GHI) is the absolute solar power that hits a horizontal ground surface. The GHI’s are classified as Diffuse Irradiance and Direct Normal Irradiance (DNI), where the energy from the ground level usually comes from the solar beam and contains a small volume of circumsolar energy [23]. Diffuse Horizontal Irradiance (DFI) energy propagates through the atmosphere. Equation (1)

^{2}to 0 [24].

#### 2.1.2. Measured Irradiance Data

#### 2.1.3. Meteorological Data

#### 2.1.4. Local Sky Imaging Data

#### 2.1.5. Mathematical Modelling

Algorithm 1 Ideal Days Selection Algorithm | |

Step 1: | Initialize $m\leftarrow 0,\text{}d\leftarrow 0,h\leftarrow 5\text{};$ |

Step 2: | While $d\le {d}_{m}$ Do |

Step 3: | While $m\le 12$ do: |

Step 4: | $I\u2254\text{}{S}^{m}\times 0.9\text{}$; |

Step 5: | $J\u2254\text{}{M}^{m}\times 0.95$; |

Step 6: | If ${s}^{d.m}<I$ Then |

Step 7: | Ideal:= False; |

Step 8: | Else If ${m}^{d.m}<J$ Then |

Step 9: | Ideal:= False; |

Step 10: | Else |

Step 11: | While $h\le 19$ do: |

Step 12: | $P\u2254\text{}avg\left({p}_{h-1}^{d,m},{p}_{h+1}^{d,m}\right)\times 0.9$; |

Step 13: | If ${p}_{h-1}^{d,m}<P$ Then |

Step 14: | Ideal:= False; |

Step 15: | Break; |

Step 16: | Else If $\left({p}_{h-1}^{d,m}-P\right)<-200$ Then |

Step 17: | Ideal:= False; |

Step 18: | Break; |

Step 19: | Else |

Step 20: | Ideal:= True; |

Step 21: | End If; |

Step 22: | End Do; |

Step 23: | End If; |

Step 24: | End Do; |

Step 24: | End Do; |

Step 25: | End |

#### 2.1.6. Deep Learning Models

- Single-Hour Model

_{ij}, over the features from the (i-1) layer, ‘m’ indexes for the feature, the weight for value at the filter’s point r, p, q is ${W}_{ijm}^{rpq}$, the time, height, and filter size are R, P, Q. With more massive convolution strides, the output layer is generally smaller, Equations (6)–(8).

**(1)**- Model Shorthand Notation: A shorthand notation is used were
- A volumetric convolutional layer with spatially sized $R\times P\times Q$ filters as $VC\left(l,R,P,Q;dr,dp,dq\right)$, implied by stride $VC\left(dr,dp,dq\right)$;
- As implied by stride $VC\left(dr,dp,dq\right)$, a volumetric max pooling with spatial size R × P × Q is specified $MP\left(R,P,Q;dr,dp,dq\right)$;
- The temporal convolutional layer has ‘n’ output nodes and is TC(n);
- FC(n) denotes the fully connected layers of ‘n’ output nodes.

**(2)**- Single-Hour Network Model: Through the shorthand notation, the whole framework for the network-constructed single-hour model is Equation (9).

- TCN

- (i)
- By design, the convolutional reuse allows using the same filter map activations and filter weights across the panel.
- (ii)
- The activation of the input feature map is used again because the same input feature map is hard to understand with different filters.
- (iii)
- The same filter weights are used on all input feature maps during batch processing.

#### 2.2. Wind Power Forecasting (WPF)

_{i}with WP as P have mutual information as I, as Equations (10) and (11)

#### Minimum Redundancy and Maximum Relevance (mRMR)-Based Feature Selection

_{i}and then combines these inputs using a suitable aggregation operator, in this case, the arithmetic mean (Algorithm 2).

Algorithm 2 M-mRMR | |

Step 1: | Initialize ${S}_{All}:=\left\{1,2,\dots ,\mathrm{G}\right\}$, $S\u2254\varnothing ,\text{}{S}_{\alpha}\u2254\varnothing $; |

Step 2: | While ${g}_{j}\in {\mathbb{M}}^{N\times T}$ Do: |

Step 3: | The F-statistic values: $F\left({g}_{j},c\right)=\frac{1}{T}\sum _{t=1}^{T}F({g}_{j}^{\left(t\right)},c)$; |

Step 4: | End Do; |

Step 5: | $\mathbb{R}\u2254\mathrm{R}oundoff\left(\alpha G\right);$ |

Step 6: | while $i<\mathbb{R}$ do: |

Step 7: | ${S}_{\alpha}\u2254{S}_{\alpha}\cup \underset{j\in \text{}{S}_{All}/{S}_{\alpha}}{Argmax}F\left({g}_{j},c\right)$; |

Step 8: | End Do; |

Step 9: | $S\u2254\text{}\underset{j\in \text{}{S}_{All}}{Argmax}F\left({g}_{j},c\right)$; |

Step 10: | For $len\left(S\right)<m$ Do: |

Step 11: | While $k\text{}\in {S}_{\alpha}/S$ Do: |

Step 12: | ${S}^{\prime}\u2254S\cup k$; |

Step 13: | ${V}_{{S}_{MWP}}=\frac{1}{\left|{S}_{NWp}\right|}{\sum}_{{f}_{i}\in {S}_{MWP}}I\left({f}_{i},P\right)$; |

Step 14: | ${W}_{{S}_{MWP}}=\frac{1}{{\left|{S}_{NWP}\right|}^{2}}{\sum}_{{f}_{i}\cdot {f}_{j}\in {S}_{MWP}}I\left({f}_{i},{f}_{j}\right)$; |

Step 15: | End While; |

Step 16: | $S\u2254S\cup \underset{k}{argmax}\frac{{V}_{F}\left({g}_{k}\right)}{{W}_{dtw}\left({g}_{k}\right)}$; |

Step 17: | End For; |

Step 18: | Return S; |

- The Ordered Weighted Averaging—Weighted Average (OWAWA) Operator

_{j}is the jth greatest of the a

_{i}and each argument of the a

_{i}has an associated weight $\left(WA\right){v}_{j}$ with $\sum _{j=1}^{n}{v}_{j}=1$ and ${v}_{j}$ ∈ [0, 1], $\widehat{{v}_{j}}=\beta {w}_{j}+\left(1-\beta \right){v}_{j}$ with $\beta \in \left[0,1\right]$, where ${v}_{j}$ is the weight (WA) ${v}_{i}$ ordered according to ${b}_{j}$, that is, the jth greatest of the a

_{i}. This work receives the OWA operator if $\beta =1$ and the WA if $\beta =0$. The OWAWA operator achieves features such as those of traditional aggregation operators. It is worth noting that this work distinguishes between ordered layers and extends them using mixture operators, among other things.

- Physics-Constrained LSTM Model (PC-LSTM)

- OWAWA-CNN-LSTM Forecasting Model

## 3. Modelling MOACO-Based DRP

#### 3.1. Objective Functions

#### 3.2. Operational Cost Function

#### 3.3. A Prototype of a Smart Grid System

#### MOACO Algorithm

_{i}(X) and h

_{i}(X) are equality and variation constraints, and ‘n’ is the number of objective functions. The possible solution for an MOOP is X or Y. The first solution will win, followed by the next, or none will control anything. As a result, if the two preceding conditions are met, one solution, ‘X’, will control the other in an optimization problem, Equation (26).

Algorithm 3 MOACO | |

Input: | Production volume, proposed energy price, DG operational and emission costs, the next day’s mean, and variance of WS and SP, and apply for demand from the daily load curve |

Step 1: | Obtaining the volume of WP and SP from the proposed statical model |

Step 2: | Set value parameters, number of ants (NA), and iterations (M) |

Step 3: | Generate a primary population as ${X}^{T}=\left[{X}_{1},\text{}{X}_{2},\text{}\dots ,\text{}{X}_{T}\right];$ |

Step 4: | Calculate fitness function: $Min{f}_{1}\left(X\right)={\sum}_{t=1}^{T}{F}^{Cost}\left(t\right)={\sum}_{t=1}^{T}COC\left(t\right)+{\sum}_{t=1}^{T}{\sum}_{s=1}^{S}P{r}_{s}\times UO{C}_{s}\left(t\right)$; |

Step 5: | Initialize Pareto archive:= $\varnothing ;$ |

Step 6: | Identify and separate non-dominated results, and store them in the Pareto archive; |

Step 7: | Initialize all pheromone values to ${\tau}_{0};$ |

Step 8: | While $i<M$ Do: |

Step 9: | While $j<NA$ Do: |

Step 10: | Identify new solution S using:$j=\left(\right)open="\{">\begin{array}{ll}\mathrm{arg}{\mathrm{max}}_{j\in {S}_{k}\left(i\right)}\left\{\left[\tau \left(i,j\right){]}^{\alpha}\right[\eta \left(i,j\right){]}^{\beta}\right\}\hfill & \text{}\mathrm{if}\text{}q\le {q}_{0}\hfill \\ J\hfill & \text{}\mathrm{otherwise}\text{}\hfill \end{array}$ ${P}_{k}\left(i,j\right)=\left(\right)open="\{">\begin{array}{ll}\frac{\left[\tau \left(i,j\right){]}^{\alpha}\right[\eta \left(i,j\right){]}^{\beta}}{{\sum}_{w\in {r}_{k}\left(i\right)}\left[\tau \left(i,u\right){]}^{\alpha}\right[\eta \left(i,u\right){]}^{\beta}}\hfill & \text{}\mathrm{if}\text{}j\in {S}_{k}\left(i\right)\hfill \\ 0\hfill & \text{}\mathrm{otherwise}.\text{}\hfill \end{array}$ |

Step 11: | For each solution in the current ant population, measure the values of the corresponding objectives; |

Step 12: | Apply to update local rule using: ${\tau}_{i,j}\left(t\right)=\left(1-{\rho}_{l}\right){\tau}_{i,j}\left(t-1\right)+{\rho}_{l}{\tau}_{0};$ |

Step 13: | End Do; |

Step 14: | Update Pareto archive; |

Step 15: | While non-dominated solution $\in $ Pareto archive Do: |

Step 16: | Apply to update global rule:$\text{}{\tau}_{i,j}\left(t\right)=\left(1-{\rho}_{g}\right){\tau}_{i,j}\left(t-1\right)+\frac{{\rho}_{g}}{P\left(S\right)+CS\left(S\right)}$ |

Step 17: | End Do; |

Step 18: | End Do; |

## 4. NWP Simulation and Analysis

^{W}/

_{m2}on the front and rear sides. Figure 11 depicts the average hourly SP sourced through the “Solar Energy Centre, Ministry of New and Renewable Energy, Government of India” [41].

^{V}/

_{kWh}. A typical system with a 40 kWh battery charges from 10% to 100% of its capacity, with a 92% charge or discharge efficiency. Table 2 lists the DRP packages that are available. For DRP to be implemented, it is predictable that about 40% more consumers will enrol.

**Case 1:**Assuming the operational cost without DRP.

**Case 2:**Assuming the operational cost of DRP.

**Case 1:**Considering the cost of operations without the proposed DRP: From here, the operational costs are minimised independently without considering the DR. Figure 12 illustrates the best power generation allocation for lower operating costs. It shows that the battery begins to be charged early in the morning when energy costs are low, and whenever energy costs are high, the utility obtains energy from SG, wherein CEs prioritize power usage only at the lowest quoted price. The outcomes in Figure 13 show that the SG does not seek SP and WP. As a result, when evaluating the reasonable operational cost, they will not get much consideration.

**Case 2:**Costs and functionality of operations using DR: With the help of DR, operating costs are reduced independently. Figure 14 demonstrates the optimal power generation unit allocation for reducing operational costs. Although SP generation decreases from 4.54 to 3.15 kW, WP generation in the DRP decreases from 8.02 to 7.41 kW. These programs reduce SP and WP generation from 47.68 to 44.65 kW and 86.10 to 84.32 kW, respectively.

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 2.**Illustration of an optimum day chosen on the left and a non-ideal day on the right (with a minor decrease at noon).

**Figure 3.**(

**a**) VCNN and TCN-based Neural Network. (

**b**) TCN Block Architecture. (

**c**) ResBlock Architecture.

**Figure 15.**Output power (

**a**) Wind power estimate without DR; (

**b**) Wind power estimate with DR; (

**c**) PV power estimate with DR.

Class | Variables |

SP | GHI, DNI, DIF, open-sky indices, spectrum energy, and neighbour energy. |

Weather Data | Conditions such as pressure, temperature, humidity, Wind Speed (WS), wind direction, rainfall, aerosol optical depth, and cloud cover |

Features of Sky Images | Cloud movement vector, cloud cover ratio, image features. |

Other | Solar zenith, azimuth, local time, solar time |

DRP-1 | kW | 0–10 | 10–30 | 30–60 | 60–100 |

₹/kWh | 1.5 | 1.8 | 2.15 | 4.5 | |

DRP-2 | kW | 0–10 | 10–40 | 40–60 | 60–80 |

₹/kWh | 1.25 | 1.6 | 3.2 | 4.75 |

Algorithms | Computation Time (ms) | Standard Deviation |
---|---|---|

MOFPA | 2 | 5 |

MOGFPA | 1 | 2 |

MOACO | 0.5 | 1 |

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## Share and Cite

**MDPI and ACS Style**

Arumugham, V.; Ghanimi, H.M.A.; Pustokhin, D.A.; Pustokhina, I.V.; Ponnam, V.S.; Alharbi, M.; Krishnamoorthy, P.; Sengan, S.
An Artificial-Intelligence-Based Renewable Energy Prediction Program for Demand-Side Management in Smart Grids. *Sustainability* **2023**, *15*, 5453.
https://doi.org/10.3390/su15065453

**AMA Style**

Arumugham V, Ghanimi HMA, Pustokhin DA, Pustokhina IV, Ponnam VS, Alharbi M, Krishnamoorthy P, Sengan S.
An Artificial-Intelligence-Based Renewable Energy Prediction Program for Demand-Side Management in Smart Grids. *Sustainability*. 2023; 15(6):5453.
https://doi.org/10.3390/su15065453

**Chicago/Turabian Style**

Arumugham, Vinothini, Hayder M. A. Ghanimi, Denis A. Pustokhin, Irina V. Pustokhina, Vidya Sagar Ponnam, Meshal Alharbi, Parkavi Krishnamoorthy, and Sudhakar Sengan.
2023. "An Artificial-Intelligence-Based Renewable Energy Prediction Program for Demand-Side Management in Smart Grids" *Sustainability* 15, no. 6: 5453.
https://doi.org/10.3390/su15065453