Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting

<table class="table-group" id="tab2"><tr><td><table class="table"><tr><td class="thead-hr" colspan="6"><hr/></td></tr><tr class="thead"><td align="left">Criterion </td><td align="center"> Persistence </td><td align="center"> BI best </td><td align="center"> CFD best </td><td align="center"> BI average </td><td align="center"> CFD average </td></tr><tr><td class="thead-hr" colspan="6"><hr/></td></tr><tr><td align="left"> RMSE </td><td align="center">2.3554</td><td align="center"> 1.6532 </td><td align="center"><span style="width: 37.1977ptpx;"><svg height="8.84376pt" id="M186" style="vertical-align:-0.2401896pt" version="1.1" viewbox="-0.0498162 -8.60357 37.1977 8.84376" width="37.1977pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><path d="M409 0V34C329 39 322 56 322 125V635C234 611 160 593 94 583V554L145 551C186 547 190 541 190 488V125C190 54 181 39 102 34V0H409Z" id="g13-47"></path><glyph.data ascent="1024" descent="-360" horiz-adv-x="500" vert-adv-y="500"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,6.774,0)"><path d="M113 -12C146 -12 170 11 170 46C170 78 146 103 114 103S58 78 58 46C58 11 82 -12 113 -12Z" id="g113-47"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="228" vert-adv-y="228"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,9.863,0)"><path d="M179 359C209 485 294 542 343 567C378 584 421 597 450 603L443 641C405 636 339 624 294 609C178 570 28 456 28 243C28 84 127 -12 253 -12C385 -12 480 89 480 215C480 328 403 402 288 402C272 402 254 394 229 382L179 359ZM234 331C302 331 349 265 349 171C349 106 335 29 273 29C220 29 166 106 166 239C166 263 168 303 172 320C192 327 211 331 234 331Z" id="g13-52"></path><glyph.data ascent="1024" descent="-360" horiz-adv-x="500" vert-adv-y="500"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,16.637,0)"><path d="M486 174V231H396V629H324C229 512 123 368 15 215V174H269V116C269 55 264 41 179 34V0H477V34C403 41 396 51 396 112V174H486ZM269 231H90C155 335 219 427 267 493H269V231Z" id="g13-50"></path><glyph.data ascent="1024" descent="-360" horiz-adv-x="500" vert-adv-y="500"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,23.411,0)"><path d="M152 515H407L439 613L433 623H115L70 303C91 310 134 322 179 322C270 322 327 264 327 174C327 100 297 45 235 45C178 45 130 84 106 106C93 117 82 120 70 112C53 99 38 76 38 61S43 36 63 20C84 2 126 -12 166 -12C226 -11 297 15 354 56C424 105 455 164 455 226C455 312 382 394 254 394C228 394 175 380 125 362L152 515Z" id="g13-51"></path><glyph.data ascent="1024" descent="-360" horiz-adv-x="500" vert-adv-y="500"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,30.185,0)"><path d="M306 382C339 403 362 417 379 433C398 450 411 473 411 500C411 578 352 635 248 635H247C181 635 134 604 106 573L56 506L82 482C117 529 151 557 201 557C246 557 287 531 287 466C287 404 228 364 143 336L150 302C165 306 186 309 204 309C258 309 324 275 324 179C324 85 285 44 235 44C175 44 128 88 106 108C92 118 80 116 69 106C56 97 39 79 39 61C39 47 46 34 63 21C83 5 123 -12 174 -12C236 -12 454 58 454 222C454 297 403 362 306 380V382Z" id="g13-49"></path><glyph.data ascent="1024" descent="-360" horiz-adv-x="500" vert-adv-y="500"></glyph.data></g></svg></span></td><td align="center"> 1.6592 </td><td align="center"> 1.6620 </td></tr><tr><td align="left">MAE </td><td align="center"> 1.8693</td><td align="center"> 1.2484 </td><td align="center"> 1.2756 </td><td align="center"><b>1.2386</b></td><td align="center">1.2717 </td></tr><tr class="table-tr"><td colspan="6"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

Wind speed forecast assessment for various cases using <svg height="12.0395pt" id="M185" style="vertical-align:-2.3616pt" version="1.1" viewbox="-0.0498162 -9.6779 64.5316 12.0395" width="64.5316pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><path d="M719 650H483V622C553 619 576 606 581 560C585 532 588 487 588 394V148H585L167 650H20V622C67 618 88 610 109 585C127 562 129 557 129 484V264C129 173 126 124 123 93C118 44 92 31 35 28V0H272V28C204 32 181 44 176 95C173 124 170 172 170 264V515H172L598 -9H629V394C629 487 631 532 635 563C639 606 663 619 719 622V650Z" id="g190-79"></path><glyph.data ascent="989" descent="-360" horiz-adv-x="743" vert-adv-y="743"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,9.55,0)"><path d="M673 0V28C608 34 594 43 563 129C499 303 432 494 370 665L339 656L132 132C98 43 84 37 20 28V0H238V28C162 35 154 45 175 108C188 150 203 192 219 237H432C454 176 474 122 487 83S491 36 428 28V0H673ZM418 280H234C265 362 296 450 328 535H330L418 280Z" id="g190-66"></path><glyph.data ascent="989" descent="-360" horiz-adv-x="691" vert-adv-y="691"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,18.911,0)"><path d="M631 18C609 24 585 35 559 65C534 91 514 117 478 169C448 214 406 281 389 313C462 346 516 399 516 485C516 545 490 590 449 616C412 641 363 650 290 650H42V622C120 615 128 612 128 527V125C128 40 120 34 38 28V0H300V28C221 34 212 40 212 125V284H244C295 284 312 272 329 244C359 195 395 133 430 84C475 19 516 -3 592 -7C603 -8 615 -8 627 -8L631 18ZM212 316V563C212 591 215 602 223 607C231 613 248 617 277 617C352 617 423 577 423 469C423 415 407 375 368 345C343 324 310 316 260 316H212Z" id="g190-83"></path><glyph.data ascent="989" descent="-360" horiz-adv-x="621" vert-adv-y="621"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,27.597,0)"><path d="M655 0V28C585 35 563 50 517 120C469 193 414 280 360 366C427 469 460 516 481 545C526 604 545 614 619 622V650H391V622C457 614 462 604 438 560C411 511 381 463 339 398C295 466 258 523 225 580C210 606 211 615 272 622V650H26V622C98 614 111 603 154 538C189 482 235 408 288 324C200 188 149 116 124 85C95 47 64 34 7 28V0H236V28C169 36 164 46 188 89C220 148 261 217 310 291C372 193 417 118 442 77C461 44 454 35 392 28V0H655Z" id="g190-89"></path><glyph.data ascent="989" descent="-360" horiz-adv-x="654" vert-adv-y="654"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,36.595,0)"><path d="M300 -147C201 -63 143 98 143 270S200 602 300 686L282 710C136 610 70 450 70 271V270C70 89 136 -72 282 -170L300 -147Z" id="g113-41"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="346" vert-adv-y="346"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,41.282,0)"><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" id="g113-49"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="480" vert-adv-y="480"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,47.786,0)"><path d="M95 130C70 130 46 113 46 88C46 72 54 64 59 64C93 55 121 33 121 -3C121 -41 93 -68 44 -88L55 -117C117 -98 186 -56 186 22C186 91 131 130 95 130Z" id="g113-45"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="228" vert-adv-y="228"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,53.146,0)"><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" id="g113-49"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="480" vert-adv-y="480"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,59.649,0)"><path d="M275 270C275 450 212 609 64 710L45 686C145 604 203 442 203 270S147 -63 45 -147L64 -170C213 -68 275 89 275 270Z" id="g113-42"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="346" vert-adv-y="346"></glyph.data></g></svg> ANNs.

International Scholarly Research Notices

tab2

Table 2

Table 2: Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting 