SIYAMSAQLAIN/remote_microgrid_data
Remote Microgrid Time-Series Dataset This dataset provides one-minute–resolution time-series traces from a remote wind–diesel–battery microgrid. It contains normalized historical values and short-term forecasts for available wind power and electrical demand. The data are anonymized but preserve the temporal structure needed for research on microgrid control and forecasting. Contents Parquet files included: norm_avail_wind_power_data.parquet — Realized available… See the full description on the dataset page: https://huggingface.co/datasets/SIYAMSAQLAIN/remote_microgrid_data.
035
1{
2 "cells": [
3 {
4 "cell_type": "code",
5 "execution_count": null,
6 "id": "bd8a90f9",
7 "metadata": {},
8 "outputs": [],
9 "source": [
10 "import pandas as pd"
11 ]
12 },
13 {
14 "cell_type": "markdown",
15 "id": "03510c55",
16 "metadata": {},
17 "source": [
18 "# Demand data"
19 ]
20 },
21 {
22 "cell_type": "code",
23 "execution_count": null,
24 "id": "d110761b",
25 "metadata": {},
26 "outputs": [],
27 "source": [
28 "demand = pd.read_parquet(\"norm_demand.parquet\")"
29 ]
30 },
31 {
32 "cell_type": "code",
33 "execution_count": null,
34 "id": "57ffa9fa",
35 "metadata": {},
36 "outputs": [],
37 "source": [
38 "demand.head()"
39 ]
40 },
41 {
42 "cell_type": "code",
43 "execution_count": null,
44 "id": "b470832e",
45 "metadata": {},
46 "outputs": [],
47 "source": [
48 "demand.tail()"
49 ]
50 },
51 {
52 "cell_type": "code",
53 "execution_count": null,
54 "id": "c563e1e9",
55 "metadata": {},
56 "outputs": [],
57 "source": [
58 "demand_pred = pd.read_parquet(\"norm_demand_forecast.parquet\")"
59 ]
60 },
61 {
62 "cell_type": "code",
63 "execution_count": null,
64 "id": "ac2d1a76",
65 "metadata": {},
66 "outputs": [],
67 "source": [
68 "demand_pred.head()"
69 ]
70 },
71 {
72 "cell_type": "code",
73 "execution_count": null,
74 "id": "dd198e95",
75 "metadata": {},
76 "outputs": [],
77 "source": [
78 "demand_pred.tail()"
79 ]
80 },
81 {
82 "cell_type": "markdown",
83 "id": "0a09a009",
84 "metadata": {},
85 "source": [
86 "# Wind data"
87 ]
88 },
89 {
90 "cell_type": "code",
91 "execution_count": null,
92 "id": "af3edc03",
93 "metadata": {},
94 "outputs": [],
95 "source": [
96 "wind_data = pd.read_parquet(\"norm_avail_wind_power_data.parquet\")"
97 ]
98 },
99 {
100 "cell_type": "code",
101 "execution_count": null,
102 "id": "f71598db",
103 "metadata": {},
104 "outputs": [],
105 "source": [
106 "wind_data.head()"
107 ]
108 },
109 {
110 "cell_type": "code",
111 "execution_count": null,
112 "id": "f4d38ad4",
113 "metadata": {},
114 "outputs": [],
115 "source": [
116 "wind_data.tail()"
117 ]
118 },
119 {
120 "cell_type": "code",
121 "execution_count": null,
122 "id": "4d878712",
123 "metadata": {},
124 "outputs": [],
125 "source": [
126 "wind_pred = pd.read_parquet(\"norm_avail_wind_power_forecast.parquet\")"
127 ]
128 },
129 {
130 "cell_type": "code",
131 "execution_count": null,
132 "id": "21648509",
133 "metadata": {},
134 "outputs": [],
135 "source": [
136 "wind_pred.head()"
137 ]
138 },
139 {
140 "cell_type": "code",
141 "execution_count": null,
142 "id": "2102b56a",
143 "metadata": {},
144 "outputs": [],
145 "source": [
146 "wind_pred.tail()"
147 ]
148 },
149 {
150 "cell_type": "code",
151 "execution_count": null,
152 "id": "527d85af",
153 "metadata": {},
154 "outputs": [],
155 "source": []
156 }
157 ],
158 "metadata": {
159 "kernelspec": {
160 "display_name": "Pytorch",
161 "language": "python",
162 "name": "python3"
163 },
164 "language_info": {
165 "codemirror_mode": {
166 "name": "ipython",
167 "version": 3
168 },
169 "file_extension": ".py",
170 "mimetype": "text/x-python",
171 "name": "python",
172 "nbconvert_exporter": "python",
173 "pygments_lexer": "ipython3",
174 "version": "3.8.16"
175 }
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177 "nbformat": 4,
178 "nbformat_minor": 5
179}
180