China’s Energy Demand Forecasting Based on the Hybrid PSO-LSSVR Model

<table class="table-group" id="tab5"><tr><td><table class="table"><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr class="thead"><td class="align_left">MAPE (%)</td><td class="align_center">Rank</td></tr><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr><td class="align_left">≤10%</td><td class="align_center">High-accuracy</td></tr><tr><td class="align_left"><span style="width: 110.066ptpx;"><svg height="9.87315pt" id="M92" style="vertical-align:-1.11981pt" version="1.1" viewbox="-0.0498162 -8.75334 110.066 9.87315" width="110.066pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M384 0V27C293 34 287 42 287 114V635C232 613 172 594 109 583V559L157 557C201 555 205 550 205 499V114C205 42 199 34 109 27V0H384Z"></path></g><g transform="matrix(.013,0,0,-0.013,6.24,0)"><path d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 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31 540 125 546 158L517 162Z"></path></g><g transform="matrix(.013,0,0,-0.013,66.343,0)"><path d="M531 71V127L115 310L531 494V550L57 335V285L531 71ZM531 -40V10H57V-40H531Z"></path></g><g transform="matrix(.013,0,0,-0.013,77.606,0)"><path d="M153 550H386L412 615L406 623H120L82 318C104 327 142 338 184 338C294 338 347 275 347 187C347 112 305 39 221 39C160 39 119 71 97 89C88 97 80 96 71 90C59 80 50 67 49 57C48 45 52 36 66 23C80 9 123 -12 169 -12C221 -11 288 15 342 59C403 109 431 165 431 225C431 308 366 395 238 395C212 395 165 379 127 364L153 550Z"></path></g><g transform="matrix(.013,0,0,-0.013,83.846,0)"><path d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z"></path></g><g transform="matrix(.013,0,0,-0.013,90.086,0)"><path d="M594 629L561 652L159 -9L190 -32L594 629ZM194 635C92 635 49 534 49 453C49 374 92 272 193 272C295 272 338 374 338 454S295 635 194 635ZM193 605C257 605 265 504 265 453C265 405 257 302 194 302C133 302 122 403 122 454S133 605 193 605ZM567 351C465 351 421 250 421 169C421 90 465 -12 566 -12S710 90 710 170S667 351 567 351ZM566 322C628 322 637 221 637 169C637 121 628 18 567 18S494 119 494 170C494 223 506 322 566 322Z"></path></g></svg></span></td><td class="align_center">Reasonable</td></tr><tr><td class="align_left">&gt;50%</td><td class="align_center">Inaccurate</td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Typical MAPE values for evaluation.</div>

Wireless Communications and Mobile Computing

tab5

Table 5

Table 5: China’s Energy Demand Forecasting Based on the Hybrid PSO-LSSVR Model 